Note: Descriptions are shown in the official language in which they were submitted.
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Image Processing Server
CROSS-REFERENCE TO RELATED APPLICATIONS
[001] This application is a continuation-in-part of U.S. non-provisional
application entitled "Image
Processing," filed May 1, 2014 and having Ser. No. 14/267,840 which, in turn,
claimed priority to
provisional application 61/956,927 filed May 1, 2013; this application further
claims priority to and
benefit of the following U.S. Provisional Patent Applications:
"Visual Search," filed April 4, 2014 and having Ser. No. 61/975,691;
"Visual Search Advertising," filed April 7, 2014 and having Ser. No.
61/976,494;
"Image Processing," filed May 1, 2014 and having Ser. No. 61/987,156;
"Real-time Target Selection in Image Processing" filed July 31, 2014 and
having Ser. No.
62/031,397;
"Distributed Image Processing" filed October 27, 2014 having Ser. No.
62/069,160; and
"Selective Image Processing" filed November 25, 2014 having Ser. No.
62/084,509.
All the above patent applications are hereby incorporated herein by reference.
BACKGROUND
[002] Field of the Invention
[003] The invention is in the field of image processing, and more particularly
in the field of
characterizing content of images.
[004] Related Art
[005] It is typically more difficult to extract information from images as
compared to text data.
However, a significant fraction of information is found in images. The
reliability of automated image
recognition systems is highly dependent on the contents of an image. For
example, optical character
recognition is more reliable than facial recognition. It is a goal of image
recognition to tag an image.
Tagging refers to the identification of tags (words) that characterize the
content of an image. For
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example an image of a car may be tagged with the words "car," "Ford Granada,"
or "White 1976 Ford
Granada with broken headlight." These tags include varying amounts of
information and, as such, may
vary in usefulness.
SUMMARY
[006] Embodiments of the invention include a two pronged approach to tagging
of images. The first
prong is to perform automated image recognition on an image. The automated
image recognition
results in a review of the image. The image review includes one or more tags
identifying contents of the
image and optionally also a measure of confidence representative of the
reliability of the automated
image recognition. The second prong in the approach to tagging of images
includes a manual tagging of
the image. Manual tagging includes a person viewing each image, considering
the content of the image,
and manually providing tags representative of the content of the image.
Automated image recognition
has an advantage in that the cost, in time or money, of analyzing each image
can be relatively low.
Manual tagging of images has an advantage of higher accuracy and reliability.
[007] Embodiments of the invention combine both automated image recognition
and manual image
recognition. In some embodiments automated image recognition is performed
first. The resulting
image review typically includes both one or more tags characterizing the image
and a measure of
confidence in the accuracy of these tags. If the confidence is above a
predetermined threshold, then
these tags are associated with the image and provided as an output of the
tagging process. If the
confidence is below the predetermined threshold, then a manual review of the
image is performed. The
manual review results in additional and/or different tags that characterize
the contents of the image. In
some embodiments, the automated image recognition and the manual review of the
image are
performed in parallel. The manual review is optionally cancelled or aborted if
the automated image
recognition results in one or more tags having a confidence above the
predetermined threshold.
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[008] In some embodiments recognition of an image can be upgraded. Upgrading
of the image
recognition process includes a request for further or improved tags
representative of the content of the
image. For example, if automated image recognition results in the tags "white
car," an upgrade of this
recognition may result in the tags "white Ford Granada." In some embodiments,
an upgraded review
makes use of an expert human reviewer. For example, the above example may
include the use of a
human reviewer with an expert knowledge of automobiles. Other examples of
reviewer expertise are
discussed elsewhere herein.
[009] Various embodiments of the invention include features directed toward
improving the accuracy
of image recognition while also minimizing cost. By way of example, these
features include efficient use
of human reviewers, real-time delivery of image tags, and/or seamless upgrades
of image recognition.
The approaches to image recognition disclosed herein are optionally used to
generate image tags
suitable for performing internet searches and/or selecting advertisements. For
example, in some
embodiments, image tags are automatically used to perform a Google search
and/or sell advertising
based on Google's Ad Words.
[00101 Various embodiments of the invention include an image processing system
comprising an I/O
configured to communicate an image and image tags over a communication
network; an automatic
identification interface configured to communicate the image to an automatic
identification system and
to receive a computer generated review of the image from the automatic
identification system, the
computer generated review including one or more image tags identifying
contents of the image;
destination logic configured to determine a first destination to send the
image to, for a first manual
review of the image by a first human reviewer; image posting logic configured
to post the image to the
destination; review logic configured to receive the a manual review of the
image from the destination
and to receive the computer generated review, the manual review including one
or more image tags
identifying contents of the image; response logic configured to provide the
image tags of the computer
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generated review and the image tags of the manual review to the communication
network; memory
configured to store the image; and a microprocessor configured to execute at
least the destination logic.
[0011] Various embodiments of the invention include a method of processing an
image, the method
comprising receiving an image from an image source; distributing the image to
an automated image
identification system; receiving a computer generated review from the
automated image identification
system, the computer generated review including one or more image tags
assigned to the image by the
automated image identification system and a measure of confidence, the measure
of confidence being a
measure of confidence that the image tags assigned to the image correctly
characterize contents of the
image; placing the image in an image queue; determining a destination; posting
the image for manual
review to a first destination, the first destination including a display
device of a human image reviewer;
and receiving a manual image review of the image from the destination, the
image review including one
or more image tags assigned to the image by the human image reviewer, the one
or more image tags
characterizing contents of the image.
[0012] Various embodiments of the invention include an image source comprising
a camera configure
to capture an image; a display configured to present the image to a user; eye
tracking logic configured to
detect an action of one or more eyes of the user; optional image marking logic
configured to place a
mark on the image, the mark being configured to indicate a particular subset
of the image and being
responsive to the detected action; display logic configured to display the
mark on the image in real time;
an I/O configured to provide the image a computer network; and a processor
configured to execute at
least the display logic.
[0013] Various embodiments of the invention include an image source comprising
a camera configure
to capture an image; a display configured to present the image to a user; eye
tracking logic configured to
detect an action of one or more eyes of the user; image marking logic
configured for a user to indicate a
particular subset of the image and to highlight an object within the subset,
the indication being
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responsive to the detected action; display logic configured to display the
highlighted on the image in
real time; an I/O configured to provide the image and the indication of the
particular subset to a
computer network; and a processor configured to execute at least the display
logic.
[0014] Various embodiments of the invention include an image source comprising
a camera configure
to capture an image; a display configured to present the image to a user;
selection logic configured for
selecting; image marking logic configured for a user to indicate a particular
subset of the image and to
highlight an object within the subset, the indication being responsive to the
detected finger; an I/O
configured to provide the image and the indication of the particular subset to
a computer network;
display logic configured to display the image in real time and to display
image tags received from the
computer network in response to the image, the image tags characterizing
contents of the image; and a
processor configured to execute at least the display logic.
[0015] Various embodiments of the invention include an image processing system
comprising an I/O
configured to communicate an image sequence and image tags over a
communication network; optional
an automatic identification interface configured to communicate the image
sequence to an automatic
identification system and to receive a computer generated review of the image
from the automatic
identification system, the computer generated review including one or more
image tags identifying
contents of the image; destination logic configured to determine a first
destination to send the image
sequence to, for a first manual review of the image sequence by a first human
reviewer; image posting
logic configured to post the image sequence to the destination; review logic
configured to receive the a
manual review of the image sequence from the destination and optionally to
receive the computer
generated review, the manual review including one or more image tags
identifying an action within of
the image sequence; response logic configured to provide the image tags of the
manual review to the
communication network; memory configured to store the image sequence; and a
microprocessor
configured to execute at least the destination logic.
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[0016] Various embodiments of the invention include a method of processing an
image, the method
comprising: receiving one or more first descriptors of an image at an image
processing server, from a
remote client via a communication network; comparing the received first
descriptors to second
descriptors stored locally to the image processing server, to determine if the
first descriptors match a
set of the second descriptors; responsive to the first descriptors matching
the set of second descriptors,
retrieving one or more image tags stored in association with the set of second
descriptors; and providing
the one or more image tags to the client.
[0017] Various embodiments of the invention include a method of processing an
image at an image
processing server, the method comprising: receiving an image and data
characterizing the image from a
remote client; determining a destination for the image, the destination being
associated with a human
image reviewer, the determination of the destination being based on a match
between the data
characterizing the image and a specialty of the human reviewer; posting the
image to the determined
destination; receiving one or more image tags characterizing the image, from
the destination; and
providing the one or more image tags to the client.
[0018] Various embodiments of the invention include a method of processing an
image, the method
comprising: receiving data characterizing the image from a mobile device, the
data characterizing the
image including identified features of an image or descriptors of an image;
generating image tags based
on the data characterizing the image; providing the image tags to the mobile
device.
[0019] Various embodiments of the invention include a method of processing an
image, the method
comprising: receiving an image using a portable device; identifying features
of the image using a
processor of the portable device; providing the features to a remote image
processing server via a
communication network; receiving image tags based on the features from the
image processing server;
and displaying the image tags on a display of the portable device.
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[0020] Various embodiments of the invention include a method of processing an
image, the method
comprising: receiving an image using a portable device; identifying features
of the image using a
processor of the portable device; deriving image descriptors based on the
identified features; providing
the descriptors to a remote image processing server via a communication
network; receiving image tags
based on the descriptors from the image processing server; and displaying the
image tags on a display of
the portable device.
[0021] Various embodiments of the invention include a method of processing an
image, the method
comprising: receiving an image using a portable device; identifying features
of the image using a
processor of the portable device; deriving image descriptors based on the
identified features; comparing
the image descriptors with a set of image descriptors previously stored on the
portable device to
determine if there is a match between the image descriptors and the stored set
of image descriptors; if
there is a match between the image descriptors and the stored set of image
descriptors retrieving one
or more image tags associated with the set of image descriptors from memory of
the portable device;
displaying the retrieved one or more image tags on a display of the portable
device.
[0022] Various embodiments of the invention include a method of processing an
image, the method
comprising: receiving an image using a portable device; identifying features
of the image using a
processor of the portable device; deriving image descriptors based on the
identified features; comparing
the image descriptors with a set of image descriptors previously stored on the
portable device to
determine if there is a match between the image descriptors and the stored set
of image descriptors;
classifying the image based on the match between the image descriptors and the
stored set of image
descriptors; sending the image and the classification of the image to a remote
image processing server;
receiving one or more image tags based on the image; and displaying the one or
more image tags on a
display of the portable device.
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[0023] Various embodiments of the invention include an image processing system
comprising an I/O
configured to communicate an image and image tags over a communication
network; an image ranker
configured to determine a priority for tagging the image; destination logic
configured to determine a
first destination to send the image to, for a first manual review of the image
by a first human reviewer;
image posting logic configured to post the image to the destination; review
logic configured to receive
the a manual review of the image from the destination, the manual review
including one or more image
tags identifying contents of the image; memory configured to store the one or
more image tags in a data
structure; and a microprocessor configured to execute at least the image
ranker.
[0024] Various embodiments of the invention include an image processing system
comprising an I/O
configured to receive an image over a communication network; an image ranker
configured to
determine a priority of the image and to determine whether or not to tag the
image based on the
priority and/or how to tag the image; manual or automatic means for tagging
the image to produce one
or more image tags characterizing the image; memory configured to store the
image and the one or
more image tags characterizing the image, in a data structure; and a
microprocessor configured to
execute at least the image ranker.
[0025] Various embodiments of the invention include an image processing system
comprising an I/O
configured to receive an image over a communication network; an image ranker
configured to
determine a priority of the image and to select a process of tagging the image
based on the priority;
means for tagging the image to produce one or more image tags characterizing
the image; memory
configured to store the image and the one or more image tags characterizing
the image, in a data
structure; and a microprocessor configured to execute at least the image
ranker.
[0026] Various embodiments of the invention include an image processing system
comprising an I/O
configured to communicate an image and image tags over a communication
network; an image ranker
configured to determine a priority for tagging the image based on how many
times a video including the
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image is viewed; destination logic configured to determine a destination to
send the image to, for a
manual review of the image by a human reviewer; image posting logic configured
to post the image to
the destination; review logic configured to receive the manual review of the
image from the destination,
the manual review including one or more image tags identifying contents of the
image; memory
configured to store the one or more image tags in a data structure; and a
microprocessor configured to
execute at least the image ranker.
[0027] Various embodiments of the invention include an method of processing an
image, the method
comprising receiving an image from an image source; distributing the image to
an automated image
identification system; receiving a computer generated review from the
automated image identification
system, the computer generated review including one or more image tags
assigned to the image by the
automated image identification system and a measure of confidence, the measure
of confidence being a
measure of confidence that the image tags assigned to the image correctly
characterize contents of the
image; assigning a priority to the image based on the measure of confidence;
determining that the
image should be manually tagged based on the priority; posting the image for
manual review to a first
destination, the first destination including a display device of a human image
reviewer; and receiving a
manual image review of the image from the destination, the image review
including one or more image
tags assigned to the image by the human image reviewer, the one or more image
tags assigned by the
human image reviewer characterizing contents of the image.
[0028] Various embodiments of the invention include an method of processing an
image, the method
comprising receiving an image from an image source; automatically determining
a priority to the image
using a microprocessor; determining how the image should be tagged based on
the priority; tagging the
image to produce one or more tags, the one or more tags characterizing
contents of the image; and
storing the image and the one or more tags in a data structure.
BRIEF DESCRIPTION OF THE DRAWINGS
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[0029] FIG. 1 illustrates an image processing system, according to various
embodiments of the
invention.
[0030] FIG. 2 illustrates an image capture screen, according to various
embodiments of the invention.
[0031] FIG. 3 illustrates search results based on an image analysis, according
to various embodiments of
the invention.
[0032] FIG. 4 illustrates methods of processing an image, according to various
embodiments of the
invention.
[0033] FIG. 5 illustrates alternative methods of processing an image,
according to various embodiments
of the invention.
[0034] FIG. 6 illustrates methods of managing a reviewer pool, according to
various embodiments of
the invention.
[0035] FIG. 7 illustrates methods of receiving image tags in real-time,
according to various
embodiments of the invention.
[0036] FIG. 8 illustrates methods of upgrading an image review, according to
various embodiments of
the invention.
[0037] FIG. 9 illustrates an example of Image Source 120A including electronic
glasses, according to
various embodiments of the invention.
[0038] FIG. 10 illustrates a method of processing an image on an image source,
according to various
embodiments of the invention.
[0039] FIG. 11 illustrates a method of processing an image based on image
descriptors, according to
various embodiments of the invention.
[0040] FIG. 12 illustrates a method of processing an image using feedback,
according to various
embodiments of the invention.
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[0041] FIG. 13 and 14 illustrates methods of providing image tags based on
image descriptors,
according to various embodiments of the invention.
[0042] FIG. 15 illustrates methods of prioritizing image tagging, according to
various embodiments of
the invention.
DETAILED DESCRIPTION
[0043] FIG. 1 illustrates an Image Processing System 110, according to various
embodiments of the
invention. Image Processing System 110 is configured for tagging of images and
may include one or
more distributed computing devices. For example, Image Processing System 110
may include one or
more servers located at geographically different places. Image Processing
System 110 is configured to
communicate via a Network 115. Network 115 can include a wide variety of
communication networks,
such as the internet and/or a cellular telephone system. Network 115 is
typically configured to
communicate data using standard protocols such as IP/TCP, FTP, etc. The images
processed by Image
Processing System 110 are received from Image Sources 120 (individually
labeled 120A, 120B, etc.).
Image Sources 120 can include computing resources connected to the internet
and/or personal mobile
computing devices. For example Image Source 120A may be a web server
configured to provide a social
networking website or a photo sharing service. Image Source 120B may be a
smart phone, camera, a
wearable camera, electronic glasses or other portable image capture device.
Image sources may be
identified by a universal resource locator, an internet protocol address, a
MAC address, a cellular
telephone identifier, and/or the like. In some embodiments Image Processing
System 110 is configured
to receive images from a large number of Image Sources 120.
[0044] Part of the image tagging performed by Image Processing System 110
includes sending images
to Destinations 125 (individually labeled 125A, 125B, etc.). Destinations 125
are computing devices of
human image reviewers and are typically geographically remote from Image
Processing System 110.
Destinations 125 include at least a display and data entry devices such as a
touch screen, keyboard
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and/or microphone. For example, Destinations 125 may be in a different
building, city, state and/or
country than Image Processing System 110. Destinations 125 may include
personal computers,
computing tablets, smartphones, etc. In some embodiments, Destinations 125
include a (computing)
application specifically configured to facilitate review of images. This
application is optionally provided
to Destinations 125 from Image Processing System 110. In some embodiments,
Image Processing
System 110 is configured for human image reviewers to log into a user account
from Destinations 125.
Destinations 125 are typically associated with an individual reviewer and may
be identified by an
internet protocol address, a MAC address, a login session identifier, cellular
telephone identifier, and/or
the like. In some embodiments, Destinations 125 include an audio to text
converter. Image tagging
data provided by a human image reviewer at a member of Destinations 125 is
sent to Image Processing
System 110. The image tagging data can include textual image tags, audio data
including verbalized
tags, and/or non-tag information such as upgrade requests or inappropriate
(explicit) material
designations.
[0045] Image Processing System 110 includes an I/O (input/output) 130
configured for communicating
with external systems. I/O 130 can include routers, switches, modems,
firewalls, and/or the like. I/O
130 is configured to receive images from Image Sources 120, to send the images
to Destinations 125, to
receive tagging data from Destinations 125, and optionally to send image tags
to Image Sources 120.
I/O 130 includes communication hardware and optionally an application program
interface (API).
[0046] Image Processing System 110 further includes Memory 135. Memory 135
includes hardware
configured for the non-transient storage of data such as images, image tags,
computing instructions, and
other data discussed herein. Memory 135 may include, for example, random
access memory (RAM),
hard drives, optical storage media, and/or the like. Memory 135 is configured
to store specific data, as
described herein, through the use of specific data structures, indexing, file
structures, data access
routines, security protocols, and/or the like.
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[0047] Image Processing System 110 further includes at least one Processor
140. Processor 140 is a
hardware device such as an electronic microprocessor. Processor 140 is
configured to perform specific
functions through hardware, firmware or loading of software instructions into
registers of Processor
140. Image Processing System 110 optionally includes a plurality of Processor
140. Processor 140 is
configured to execute the various types of logic discussed herein.
[0048] Images received by Image Processing System 110 are first stored in an
Image Queue 145. Image
Queue 145 is an ordered list of images pending review, stored in a sorted
list. Images stored in Image
Queue 145 are typically stored in association with image identifiers used to
reference the images and
may have different priorities. For example, images received from a photo
sharing website may have
lower priority than images received from a smartphone. Generally, those images
for which a requester
is waiting to receive image tags representing an image in real-time are given
higher priority relative to
those for which the image tags are used for some other purpose. Image Queue
145 is optionally stored
in Memory 135.
[0049] Within Image Queue 145 images are optionally stored in association with
an image identifier or
index, and other data associated with each image. For example, an image may be
associated with
source data relating to one of Image Sources 120. The source data can include
geographic information
such as global positioning system coordinates, a street and/or city name, a
zip code, and/or the like.
The source data may include an internet protocol address, a universal resource
locator, an account
name, an identifier of a smartphone, and/or the like. Source data can further
include information about
a language used on a member of Image Sources 120, a requested priority, a
search request (e.g., an
request to do an internet search based on image tags resulting from the
image), and/or the like.
[0050] In some embodiments, an image within Image Queue 145 is stored in
association with an
indication of a particular subset of the image, the subset typically including
an item of particular
interest. For example, a requestor of image tags may be interested in
obtaining image tags relating to
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the contents of a particular subset of an image. This can occur when an image
includes several objects.
To illustrate, considering an image of a hand with a ring on one of the
fingers, the user may wish to
designate the ring as being a particular area of interest. Some embodiments of
the invention include an
application configured for a user to specify the particular item of interest
by clicking on the object or
touching the object on a display of Image Source 120B. This specification
typically occurs prior to
sending the image to Image Processing System 110.
[0051] If an image is stored in association with an indication that a
particular subset of the image is of
particular importance, then an Image Marking Logic 147 is optionally used to
place a mark on the image.
The mark being disposed to highlight the particular subset. This mark may be
made by modifying pixels
of the image corresponding to the subset and this mark allows a human image
reviewer to focus on the
marked subset. For example, the image may be marked with a rectangle or circle
prior to the image
being posted to one or more of Destinations 125. For example, highlighting a
subset of the image or an
object within the image can include applying a filter to the object or subset,
and/or changing a color of
the object or subset. In alternative embodiments, Image Marking Logic 147 is
included within an
application configured to execute on one or more of Image Sources 120 or
Destinations 125. Image
Marking Logic 147 includes hardware, firmware, and/or software stored on a non-
transient computer
readable medium. As discussed elsewhere herein, Marking Logic 147 is
optionally configured to place a
mark on the image in real-time, as the image is being generated.
[0052] In some embodiments, Marking Logic 147 is configured to use image
features detected within
an image to identify particular objects that may be marked. The detection of
image feature is discussed
elsewhere herein and is optionally part of image processing that occurs on the
client side, e.g., on Image
Source 120A. For example, features such as edges may be detected using a
processor of Image Source
120A. These features can first be used in highlighting objects for detection
and then also sent from
Image Source 120A to Image Processing System 110 where they are then used to
generate image
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descriptors as part of processing the image. In this way automated processing
of the image is
distributed between Image Source 120A, Image Processing System 110 and/or
Automatic Identification
System 152.
[0053] Under the control of Processor 140, images within Image Queue 145 are
provided to an
Automatic Identification Interface 150. The images are provided thus as a
function of their priority and
position in Image Queue 145. Automatic Identification interface 150 includes
logic configured to
communicate the image, and optionally any data associated with the image, to
an Automatic
Identification System 152. The logic is hardware, firmware, and/or software
stored on a computer
readable medium. Automatic Identification Interface 150 is further configured
to receive a computer
generated review of the image from Automatic Identification System 152, the
computer generated
review including one or more image tags identifying contents of the image. In
some embodiments,
Automatic Identification Interface 150 is configured to communicate the image
and data via Network
115 in a format appropriate for an application programming interface (API) of
Automatic Identification
System 152. In some embodiments, Automatic Identification System 152 is
included within Image
Processing System 110 and Automatic Identification Interface 150 includes, for
example, a system call
within an operating system or over a local area network.
[0054] Automatic Identification System 152 is a computer automated system
configured to review
images without a need for human input on a per picture basis. The output of
Automatic Identification
System 152 is a computer generated image review (e.g., a review produced
without human input on a
per picture basis.) Rudimentary examples of such systems are known in the art.
See, for example,
Kooaba, Clarifai, AlchemyAPI and Catchoom. Automatic Identification System 152
is typically configured
to automatically identify objects within a two dimensional image based on
shapes, characters and/or
patterns detected within the image. Automatic Identification System 152 is
optionally configured to
perform optical character recognition and/or barcode interpretation. In some
embodiments, Automatic
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Identification System 152 is distinguished from systems of the prior art in
that Automatic Identification
System 152 is configured to provide a computer generated review that is based
on the image subset
indication(s) and/or image source data, discussed elsewhere herein.
[0055] Automatic Identification System 152 is optionally configured to
determine if a copy of the image
received from a different image source has already been tagged. For example,
the same image may be
included in multiple webpages. If the image is extracted from a first of these
webpages and tagged,
Automatic Identification System 152 may recognize that the image has already
been tagged and
automatically assign these tags to each instance of the image found.
Recognizing that an image has
already been tagged optionally includes comparing the image, a part of the
image, or data
representative of the image to a database of previously tagged images. The
image may have been
previously tagged automatically or manually.
[0056] In addition to one or more image tag(s), a computer generated review
generated by Automatic
Identification System 152 optionally includes a measure of confidence
representative of a confidence
that the one or more image tags correctly identify the contents of the image.
For example, a computer
generated review of an image that is primarily characters or easily
recognizable shapes may have a
greater confidence measure than a computer generated review of an image that
consists of abstract or
ill-defined shapes. Different automated image recognition systems may produce
different confidence
levels for different types of images. Automatic Identification Interface 150
and Automatic Identification
System 152 are optional in embodiments in which automatic identification is
performed by a third party.
[0057] Image Processing System 110 further includes a Reviewer Pool 155 and
Reviewer Logic 157
configured to manage the Reviewer Pool 155. Reviewer Pool 155 includes a pool
(e.g., group or set) of
human image reviewers. Each of the human image reviewers is typically
associated with a different
member of Destinations 125. For example, each of the different members of
Destinations 125 may be
known to be operated by a different human image reviewer or to be logged into
an account of a
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different human image reviewer. Memory 135 is optionally configured to store
Reviewer Pool 155. In
some embodiments, the human image reviewers included in Reviewer Pool 155 are
classified as "active"
and "inactive." For the purposes of this disclosure, an active human image
reviewer is considered to be
one that is currently providing image tags or has indicated that they are
prepared to provide image tags
with minimal delay. In embodiments that include both active and inactive human
image reviewers, the
active reviewers are those that are provided image for review. The number of
active reviewers may be
moderated in real-time in response to a demand for image reviews. For example,
the classification of a
human image reviewer may be changed from inactive to active based on a number
of unviewed images
in Image Queue 145. An inactive reviewer is one that is not yet active, that
has let the review of an
image expire, and/or has indicated that they are not available to review
images. Inactive reviewers may
request to become active reviewers. Inactive reviewers who have made such a
request can be
reclassified as active human image reviewers when additional active human
image reviewers are
needed. The determination of which inactive reviewers are reclassified as
active reviewers is optionally
dependent on a reviewer score (discussed elsewhere herein).
[0058] Reviewer Logic 157 is configured to manage Reviewer Pool 155. This
management optionally
includes the classification of human image reviewers as active or inactive.
For example, Reviewer Logic
157 may be configured to monitor a time that a human image reviewer takes to
review an image and, if
a predetermined maximum review time (referred to herein as an image expiration
time), changing the
classification of the human image reviewer from active to inactive. In another
example, Reviewer Logic
157 may be configured to calculate a review score for a human image reviewer.
In some embodiments,
the review score is indicative of the completeness, speed and/or accuracy of
image reviews performed
by the particular human image reviewer. The review score can be calculated or
changed based on
review times and occasional test images. These test images may be, for example
images placed in Image
Queue 145 that have been previously reviewed by a different human image
reviewer. The review score
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may also be a function of monetary costs associated with the human image
reviewer. Reviewer Logic
157 includes hardware, firmware, and/or software stored on a non-transient
computer readable
medium. In some embodiments, reviewer scores are manually determined by human
moderators.
These human moderators review images and the tags assigned to these images by
human image
reviewers. Moderators are optionally sent a statistical sampling of reviewed
images and they assign a
score to the tagging of the images. This score is optionally used in
determining reviewer scores.
[0059] In some embodiments, Reviewer Logic 157 is configured to monitor status
of human image
reviewers in real-time. For example, Reviewer Logic 157 may be configured to
monitor the entry of
individual words or keystrokes as entered by a reviewer at Destination 125A.
This monitoring can be
used to determine which reviewers are actively reviewing images, which
reviewers have just completed
review of an image, and/or which reviewers have not been providing tag input
for a number of seconds
or minutes. The entry of tag words using an audio device may also be monitored
by Reviewer Logic 157.
[0060] In some embodiments, members of Reviewer Pool 155 are associated with a
specialty in which
the human image reviewer has expertise or special knowledge in. For example, a
reviewer may be an
expert in automobiles and be associated with that specialty. Other specialties
may include art, plants,
animals, electronics, music, food medical specialties, clothing, clothing
accessories, collectables, etc. As
is discussed elsewhere herein, a specialty of a reviewer may be used to select
that reviewer during an
initial manual review and/or during a review upgrade.
[0061] The review score and/or specialty associated with a human image
reviewer are optionally used
by Reviewer Logic 157 to determine which inactive reviewer to make active,
when additional active
reviewers are required. Reviewer Logic 157 includes hardware, firmware, and/or
software stored on a
non-transient computer readable medium.
[0062] Image Processing System 110 further includes Destination Logic 160.
Destination Logic 160 is
configured to determine one or more destinations (e.g., Destinations 125) to
send an image to for
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manual review. Each of Destinations 125 is associated with a respective human
image reviewer of
Reviewer Pool 155. The determinations made by Destination Logic 160 are
optionally based on
characteristics of the human image reviewer at the determined destination. The
destination may be a
computing device, smartphone, tablet computer, personal computer, etc. of the
human image reviewer.
In some embodiments, the destination is a browser from which the reviewer has
logged into Image
Processing System 110. In some embodiments, determining the destination
includes determining an
MAC address, session identifier, internet protocol and/or universal resource
locator of one of
Destinations 125. Destination Logic 160 includes hardware, firmware and/or
software stored on a non-
transient computer readable medium.
10063] Typically, Destination Logic 160 is configured to determine
Destinations 125 associated with
active rather than inactive human image reviewers as determined by Reviewer
Logic 157. Destination
Logic 160 is also typically configured to determine Destinations 125 based on
review scores of reviewers.
For example, those reviewers having higher reviewer scores may be selected for
higher priority reviews
relative to reviewers having lower reviewer scores. Thus, the determination of
a member of
Destinations 125 can be based on both reviewer scores and image review
priority.
[0064] In some embodiments, Destination Logic 160 is configured to determine
one or more members
of Destinations 125 based on the real-time monitoring of the associated
reviewers' input activity. As
discussed elsewhere herein, this monitoring may be performed by Reviewer Logic
157 and can include
detection of individual words or keystrokes entered by a human image reviewer.
In some
embodiments, Destination Logic 160 is configured to favor selecting
Destination 125A at which a human
image reviewer has just completed a review of an image relative to Destination
1256 at which a human
image reviewer is currently typing image tags on a keyboard.
[0065] In some embodiments, Destination Logic 160 is configured to use image
tags received via
Automatic Identification System 152 to determine one or more members of
Destinations 125. For
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example, if an image tag of "car" is received via Automatic Identification
Interface 150 then Destination
Logic 160 can use this information to select a member of Destinations 125
associated with a human
image reviewer that has a specialty in automobiles.
[0066] The value of an image review may also be considered in the selection of
a destination for
manual review. For example, an image review of high value may lead to the
determination of a
destination associated with a human image reviewer having a relatively high
review score, while an
image review of lower value may lead to the determination of a destination
associated with a human
image reviewer having a relatively lower review score. In some embodiments,
for some image reviews,
Destination Logic 160 is configured to select among Destinations 125 so as to
minimize a time required
to review an image, e.g., to minimize a time until the image tags of the
manual review are provided to
Network 115.
[0067] Destination Logic 160 is optionally configured to determine multiple
destinations for a single
image. For example, a first destination may be selected and then, following an
upgrade request, a
second destination may be determined. The upgrade request may come from the
Image Source 120A or
from a human image reviewer associated with the first destination. In some
embodiments, Destination
Logic 160 is configured to determine multiple destinations, to which the image
will be posted to in
parallel. For example, two, three or more destinations, each associated with a
different human image
reviewer, may be determined and the same image posted to all determined
destinations in parallel. As
used in this context, "in parallel" means that the image is posted to at least
a second destination before
any part of a review is received from the first destination.
[0068] In various embodiments, there are a variety of reasons that two or more
destinations may be
determined by Destination Logic 160. For example, a request for an upgraded
review may require a
human image reviewer having a particular specialty. Referring to the
automotive example, an image
that is first tagged with the tag "white car" may result in an upgrade request
for more information.
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Destination Logic 160 may be configured to then select a destination
associated with a human image
reviewer have a specialty in automobiles, e.g., a reviewer who can provide the
tags "1976 Ford
Granada." An upgrade request indicates that the image is subject to further
review, e.g. the image
requires or may benefit from further review. The upgrade request may be
represented by a computing
object such as a flag, command or data value, etc.
[0069] Another instance that may require a second destination occurs when the
manual review of an
image takes too long. Typically, the tagging of an image should occur within
an allotted time period or
the review is considered to expire. The allotted time period is optionally a
function of the priority of the
image review. Those reviews that are intended to occur in real-time may have a
shorter time period
relative to lower priority reviews. If the review of an image expires, Image
Processing System 110 is
optionally configured to provide the image to an additional human image
reviewer associated with a
destination determined by Destination Logic 160.
[0070] Another instance that may require a second destination occurs when a
first human reviewer
makes an upgrade request. For example, the request to upgrade the review
resulting in a tag of "car"
may come from the human image reviewer that provided the tag "car." While this
example is simplistic,
other examples may include images of more esoteric subject matter such as
packaged integrated
circuits.
[0071] Image Processing System 110 further includes Image Posting Logic 165
configured to post
images for manual review to Destinations 125 determined by Destination Logic
160. Posting typically
includes communicating the images to one or more Destinations 125 via Network
115. In various
embodiments, Image Posting Logic 165 is further configured to provide
information associated with the
image to Destinations 125. For example, Image Posting Logic 165 may post,
along with the image, an
indication of a subset of the image (e.g., subset identification), an image
marked by Image Marking Logic
147, information identifying a source of the image (e.g., source data
discussed elsewhere herein), a
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priority of the review of the image, an image expiration period, location
information associated with the
image, and/or the like. As discussed elsewhere herein, source data can
includes a universal resource
locator, global positioning coordinates, longitude and latitude, an account
identifier, an internet
protocol address, a social account, an photo sharing account, and/or the like.
[0072] In some embodiments Image Posting Logic 165 is configured to provide an
image for manual
review to more than one of Destinations 125 at the approximately the same
time. For example, an
image may be provided to Destination 125A and Destination 125B in parallel.
"Parallel delivery" means,
for example, that the image is provided to both Destinations 125A and 125B
before tagging information
is received back from either of these Destinations 125.
[0073] In some embodiments, Image Posting Logic 165 is configured to provide
an image for manual
review to one or more of Destinations 125 prior to receiving image tags from
Automatic Identification
System 152. Alternatively, in some embodiments, Image Posting Logic 165 is
configured to wait until a
computer generated review for the image is received from Automatic
Identification System 152, prior to
posting the image to one or more of Destinations 125. In these embodiments,
the computer generated
review (including image tags) is optionally also posted to the one or more of
Destinations 125 in
association with the image.
[0074] Image Posting Logic 165 is optionally configured to post identifiers of
images along with the
images. Image Posting Logic 165 includes hardware, firmware and/or software
stored on a non-
transient computer readable medium.
[0075] Image Processing System 110 further includes Review Logic 170
configured to manage the
manual and automated reviews of images. This management includes monitoring
progress of reviews,
receiving reviews from Automatic Identification System 152 and/or Destinations
125. The received
reviews include image tags as discussed elsewhere herein. In some embodiments,
Review Logic 170 is
configured to control posting of the image to one of Destinations 125 based on
a measure of
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,
confidence. The measure of confidence being representative of a confidence
that one or more image
tags already received are correct. These one or more image tags may be
received from Automatic
Identification System 152 and/or one of Destinations 125. For example, in some
embodiments if the
confidence of an image review by Automatic Identification System 152 is
greater than a predetermined
threshold, then Review Logic 170 may determine that manual review of the image
is not necessary. The
predetermined threshold can be a function of the value of the image review, of
the priority of the image
review, of the number and quality of the available Destinations 125, and/or
the like. Review Logic 170
includes hardware, firmware, and/or software stored on a non-transient
computer readable medium.
[0076] In some embodiments, if an image was sent to Automatic Identification
System 152 in parallel
with being sent to one or more of Destinations 125, then the receipt of a
review from Automatic
Identification System 152 having a confidence above a predetermined threshold
may result in
cancellation of the manual review at the one or more of Destinations 125 by
Review Logic 170.
Likewise, if an image is sent to multiple Destinations 125 in parallel, and an
image review is received
from a first of these Destinations 125, then Review Logic 170 is optionally
configured to cancel the
review requests for the image at the other Destinations 125. In some
embodiments, Review Logic 170 is
configured to cancel the review request at the other Destinations 125 once a
keystroke or word is
received from the first of the Destinations 125.
[0077] In some embodiments Review Logic 170 is configured to monitor activity
of a human image
reviewer in real-time. This monitoring can include receiving review inputs
from Destinations 125 on a
word by word or individual keystroke basis. As discussed elsewhere herein, the
words and/or
keystrokes are optionally passed on to one of Image Sources 120 as they are
received by Review Logic
170. The monitoring of a manual reviewer's activity can be used to determine
when the review of an
image expires and/or the progress in completing a manual image review. The
status of a human image
reviewer may be provided by Review Logic 170 to Reviewer Logic 157 in real-
time. Using this status,
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Reviewer Logic 157 may change the status of the reviewer from active to
inactive, adjust a stored review
score of the reviewer, establish or change a specialty for the reviewer,
and/or the like.
[0078] In some embodiments Review Logic 170 is configured to control posting
of images to
Destinations 125 by receiving measures of confidence (e.g., of the accuracy of
image reviews) and
sending responsive signals to Destination Logic 160 and/or Image Posting Logic
165. As such, Review
Logic 170 can be configured to control posting of an image to one or more of
Destinations 125 based on
a measure of confidence. The measure of confidence being representative of a
confidence that one or
more image tags correctly identify the contents of the image. In some
embodiments, Review Logic 170
is configured to receive reviews from manual image reviewers that include
information other than
image tags. For example, Review Logic 170 may receive an upgrade request from
a human image
reviewer and cause an upgraded image review to be requested. Review Logic 170
is optionally
configured to process other non-tag information received in a manual or
computer generated review.
This information can include identification of the image as being improper
(e.g., obscene), identification
of the image as containing no identifiable objects, identification of the
image as having been sent to a
reviewer of the wrong specialty, and/or the like.
[0079] In some embodiments, Review Logic 170 is configured to adjust the
confidence of an image
review by comparing image reviews of the same image from multiple sources.
These image reviews may
all be computer generated, all be manual reviews, or include at least one
computer generated review
and at least one manual review.
[0080] In some embodiments, Review Logic 170 is configured to provide image
tags received as part of
a first (computer generated or manual) review and to provide the received
image tags to a human image
reviewer at Destinations 125B. An agent (e.g., a browser or special purpose
application) executing on
Destination 125B is optionally configured to provide the image tags of the
first review to a display of
Destination 125B. In this manner, the human image reviewer at Destination 125B
can edit (add to,
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delete and/or replace) the image tags of the first review. For example, image
tags received from
Destination 125A may be provided to Destination 125B for modification.
[0081] In some embodiments, Review Logic 170 is configured to calculate review
scores based on the
results of image reviews received from Destinations 125, the time taken for
these image reviews, and
the accuracy of these image reviews.
[0082] In some embodiments Review Logic 170 is configured to provide image
reviews to a source of
the image, e.g., one of Image Sources 120, using a Response Logic 175. The
image reviews may be
provided when the image review is complete, on a character by character basis,
or on a word by word
basis. When provided on a character by character basis or a word by word
basis, the image tags are
optionally provided to the source of the image as the characters or words are
received from a human
image reviewer. Optionally Response Logic 175 is configured to provide the
image review via Network
115.
[0083] Image reviews are not necessarily returned to one of Image Sources 120.
For example, if Image
Source 120A is a photo sharing service or a social networking website, image
reviews of images from
Image Source 120A may be stored in association with an account on the photo
sharing service or the
social networking website. This storage can be in Memory 135 or at a location
external to Image
Processing System 110, such as at a webserver hosting the website. Image
reviews are optionally both
returned to one of Image Sources 120 and stored elsewhere.
[0084] In some embodiments, Response Logic 175 is configured to execute a
search based on image
tags received in a computer generated and/or manual image review. The results
of this search can be
provided to a source of the image, e.g., Image Source 120A or 120B. For
example, in some
embodiments a user uses a smartphone to create an image with a camera of Image
Source 120A. The
image is provided to Image Processing system 110 which generates an image
review of the image using
Automatic Identification System 152 and Destination 125A. The image review
includes image tags that
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.,
are then automatically used to perform an internet search (e.g., a google or
yahoo search) on the image
tags. The results of this internet search are then provided to the user's
smartphone.
[0085] In some embodiments, Response Logic 175 is configured to provide image
tags of a computer
generated and/or manual review to an Advertising System 180. Advertising
System 180 is configured to
select advertisements based on the image tags. The selected advertisements are
optionally provided to
the source of the image used to generate the image tags. For example, Response
Logic 175 may provide
the tags "1976 Ford Granada with broken headlight" to Advertising System 180
and, in response,
Advertising System 180 may select advertisements for replacement headlights.
If the source of the
image used to generate these tags is a website, the advertisements may be
displayed on the website.
Specifically, if the source of the image is an account on a photo sharing or
social networking website,
then the advertisements may be displayed on that account. Advertising System
180 is optionally
included in Image Processing System 110. Advertising System 180 is optionally
configured to take bids
for providing advertising in response to specific tags. Advertising System 180
optionally includes
Google's Adwords.
[0086] Image Processing System 110 optionally further includes Content
Processing Logic 185
configured to extract images for tagging from members of Image Sources 120.
Content Processing Logic
185 is configured to parse webpages including images and optionally text, and
extract images from
these webpages for tagging. The resulting image tags may then be provided to
Advertising System 180
for selection of advertisements that can be placed on the webpage from which
the image was extracted.
In some embodiments, Content Processing Logic 185 is configured to emulate
browser functions in
order to load images that would normally be displayed on a webpage. These
images may be displayed
on a webpage associated with a specific account, a social networking site, a
photo sharing site, a
blogging site, a news site, a dating site, a sports site, and/or the like.
Content Processing Logic 185 is
optionally configured to parse metadata tags in order to identify images.
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[0087] Content Processing Logic 185 is optionally configured to parse text
disposed on the same
webpage as an image. This text may be used by Automatic Identification System
152 in tagging of the
image, in combination with content of the image. For example, Content
Processing Logic 185 may be
configured to identify a caption for an image, comments made about an image,
text referring to the
image, webpage title or headings, people or objects tagged within an image,
text within an image (as
determined by optical character recognition (OCR)), and/or the like. The text
parsed by Content
Processing Logic 185, or a subset thereof, may be used to improve quality
and/or speed of tagging. The
text parsed is provided to Automatic Identification System 152 and or provided
to one of Destinations
125 for tagging by a human reviewer. In some embodiments Automatic
Identification System 152 is
configured to use the provided text in the generation of tags for the image.
For example, the provided
text may be used to provide context, identify a lexicon, ontology, language,
and/or information that
improves the accuracy, precision, computational efficiency, and/or other
quality of automatically and/or
manually generated image tags. The provided text is typically not relied on
solely as a source of the
generated tags, but is used as an input to improve the processing of the
image. As such, the resulting
tags may include words other than those found in the provided text.
[0088] In some embodiments, Image Posting Logic 165 is configured to provide
both an image and text
found on the same webpage as the image to Destinations 125. For example, an
image of a girl and a
bicycle at a park may have a caption "Mountain Bike Sale" or a comment "Happy
Birthday Julie." At
Destination 125 this text may be presented to a human reviewer together with
the image. The human
reviewer may use this information to better understand the focus and/or
context of the image, and
thereby provide better image tags. Likewise, in some embodiments, Automatic
Identification Interface
150 is configured to provide both an image and text fund on the same webpage
as the image to
Automatic Identification System 152. At Automatic Identification System 152
the provided text is used
to improve the automated tagging of the image based on contents of the image.
In the above example,
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,
,
the provided text may suggest to Automatic Identification System 152 that
emphasis should be placed
on the bike or on Julie. This may result in such widely different tags as
"Schwinn Bike" or "Birthday Girl."
[0089] Image Processing System 110 optionally further includes an Image Ranker
190. Image Ranker
190 is configured to determine a rank (e.g., priority) for tagging an image.
The priority may be used to
determine how or if at all to tag an image. The determination of priority may
be based on , for example,
a source of the image, a number of times the image is loaded onto a webpage, a
position of the image
on a webpage, a number of times the image is viewed on a webpage, a number of
webpages on which
an image included, a ranking of one or more webpage including the image, an
identity of a webpage
including the image, a ranking of a second image on the webpage including the
image, an owner of
webpage including the image, a domain name of a webpage including the image, a
keyword on a
webpage including the image, text found on a webpage including the image,
metadata found on a
webpage including the image, a number of times the image is clicked on the
webpage, a number of
times other images are clicked on the webpage, whether the image is part of a
video, image tags
automatically generated using Automatic Identification System 152, any
combination of these examples,
and/or the like. Image Ranker 190 includes logic in the form of hardware,
firmware, and/or software
stored on a computer readable medium. Image Ranker 190 includes logic in the
form of hardware,
firmware, and/or software stored on a computer readable medium. In various
embodiments, the
priority determined by Image Ranker 190 includes two levels (tag or no-tag),
three levels (automatic
tagging, manual tagging, or no-tag), ten priority levels, or some other
ranking scheme.
[0090] Destination Logic 160 is optionally configured to select a destination
of manual tagging of an
image based on the priority of the image.
[0091] In those embodiments, wherein a number of times the image is loaded
onto a webpage is used
to determine priority, the number may be per a fixed time period such as per
day or per month. The
number can be determined by including a line of Java or HTML script on the
webpage, as is well known
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in the art. The position of the image on the webpage may be considered as some
images may require
that a viewer scroll down before the image is viewed. As such, the number of
times the image is actually
viewed may be used to calculate the image's priority. Typically, greater
priority is assigned to images
that are viewed more often. Image Ranker 190 is optionally configured to
assign priority to an image
based on a number of times the image is clicked on the webpage or on other
webpages, and/or a
number of times other images are clicked on the webpage. Image Ranker 190 is
optionally configured to
determine priority based on a number of times an image is viewed on more than
one webpage. For
example, if the image is found on 25 different webpages, then the sum of the
views on all the webpages
may be used to determine priority for the image. In some embodiments Image
Ranker 190 is configured
to determine priority based on a number of times an image is loaded in a
browser.
[0092] Popular images may be included in a number of webpages. For example an
image that is widely
shared on a social media website may be included on numerous webpages. Image
Ranker 190 may be
configured to calculate the priority of an image as a function of the number
of webpages on which it is
included and/or the number of webpages that include a link to the image. Image
Ranker 190 is
optionally configured to identify an image as being included on multiple,
possibly otherwise unrelated,
webpages. In some embodiments Image Ranker 190 is configured to use a third
party service, such as
TinEye.com, to determine the number of webpages on which an image is located.
Typically, the greater
the number of webpages on which an image is included, the greater the priority
assigned to the image.
[0093] In some embodiments, Image Ranker 190 is configured to calculate a
priority of an image based
on a ranking of one or more webpages that include the image. For example, if a
webpage is highly
ranked in a search engine, is linked to by a significant number of other
webpages, or well ranked on
some other criteria, then an image on the webpage may be given a priority that
is a function of the
webpages' ranking. Typically the higher ranking a webpage has the greater
priority is assigned to an
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image on the webpage. Webpage ranking is optionally obtained from a third
party source, such as a
search engine.
[0094] Image Ranker 190 is optionally configured to assign a priority to an
image based on an identity
of a webpage including the image. For example, an image on a home page for a
URL may be assigned
greater priority than an image at another webpage for the same website.
Further, images may be
assigned a priority based on specific types of webpages on which the image is
included. For example,
images on social networking websites may be given higher priority relative to
images on company
websites or personal blogs. In another example, images on reference webpages
such as dictionary.com
or Wikipedia.com may be give higher priority relative to some other types of
webpages. The priority
assigned to an image is optionally based on the identity of an owner of the
webpage.
[0095] In some embodiments, Image Ranker 190 is configured to determine a
priority of a first image
on a webpage as a function of the priority of a second image on same webpage.
For example, if the
second image has a high priority the priority of the first image may be
increased accordingly.
[0096] Image Ranker 190 is optionally configured to assign a priority to an
image based on other
contents of a webpage on which the image is included. For example, if the
webpage includes text
and/or metadata the presence of specific terms or keywords in this text or
metadata may be used to
assign the priority of the image. Specifically, if a webpage includes a
valuable keyword then an image on
that webpage may be assigned a higher priority. The estimated monetary value
of a keyword is
associated with the value of the word for advertising or some other purpose,
e.g., a word that has value
on Google's Adwords . An image on a webpage that includes terms that would be
valued highly as
Adwords may be assigned a proportionally high priority. The frequency of use
of these terms as well as
their number on a webpage may also be considered by Image Ranker 190 in
determining image priority.
The text and/or metadata considered may be included in the URL of the webpage,
within a figure
caption, within a comment made on a figure, within a tag assigned to the image
by a third party, near
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text referring to the image, a person's name, a brand name, a trademark, a
corporate name, and/or the
like.
[0097] In some embodiments, Image Ranker 190 is configured to receive text
derived from an image
using optical character recognition and to determine a priority for the image
based on this text. For
example, Image Ranker 190 may receive text generated by processing an image
using Automatic
Identification system 152, and assign a priority to the image based on this
text. In some embodiments,
Image Ranker 190 is configured to give a higher priority to a first image on a
webpage, relative to images
that occur further down the webpage.
[0098] Image Ranker 190 is further configured to determine how, if at all, to
tag an image based on the
assigned priority. For example, images of lowest priority may not be tagged at
all. Images with
somewhat higher priority may be tagged using Automatic Identification System
152, and image with yet
higher priority may be tagged by a human reviewer at one of Destinations 125.
Those images having
priority sufficiently high to be tagged by a human reviewer are optionally
further divided into higher and
lower priority groups wherein images in the higher priority group are given
more attention and tagged
more thoroughly or carefully by the human reviewer. Image Posting Logic 165 is
optionally configured
to provide an indication of the priority of an image, along with the image, to
members of Destinations
125.
[0099] In some embodiments, images are first processed using Automatic
Identification System 152.
Then the images may be sent to one or more members of Destinations 125 based
on both a priority for
the image and a confidence in the automated tagging performed by Automatic
Identification System
152. For example, if the image has relatively low priority then the confidence
standard for sending the
image to a human reviewer is set relatively low. (A low confidence standard
meaning that the
automated tags are likely to be deemed sufficient and the image not sent for
human analysis.) If the
image has a relatively high priority then the confidence standard for sending
the image to a human
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=
reviewer is relatively higher. Thus, high priority images require a greater
confidence to rely just on the
automated tagging and are more likely to be sent to a human reviewer.
[00100]The processing paths that may be selected by Image Ranker 190 for an
image include, for
example, a) not tagging at all, b) tagging using just Automatic Identification
System 152, c) tagging using
Automatic Identification System 152 with optional human follow-up based on the
importance and/or
confidence of the resulting tags. d) automated tagging followed by human
review of the automated
tags, e) tagging by a human reviewer, and/or f) tagging by a human reviewer
based on a suggested level
of attention to be given by the human reviewer. These processing paths are, at
least in part, selected
based on the priority assigned to the image by Image Ranker 190. Any
combination of these processing
paths may be found in various different embodiments. In some embodiments, the
result of controlling
the type of processing used to tag an image results in those images that are
potentially more valuable to
have a greater probability of being tagged. As a result, the human tagging
resources are applied to the
highest priority ¨ most valuable images.
[00101] In some embodiments, Image Ranker 190 is configured to assign a
priority for an image based
on how often an advertisement displayed adjacent to or over an image is
clicked on. For example, if an
image is on a frequently viewed webpage, but advertisements placed over the
image are rarely clicked,
then the image may be given a relatively high priority for tagging. In this
example, an image may be
tagged more than once. If advertisements based on initial tags are not clicked
on with an expected
frequency, then the image may be retagged. Retagging is optionally performed
by a human reviewer
who receives, via Image Posting Logic 165, the image and the initial
(inadequate) tags. The human
reviewer can use this information to provide improved tags.
[00102] FIG. 2 illustrates an Image Capture Screen 210, according to various
embodiments of the
invention. Image Capture Screen 210 as illustrated is generated by, for
example, an application
executing on a smartphone, electronic glasses or other Image Source 120. image
Capture Screen 210 is
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,
,
,
includes features configured to capture an image, mark a specific area of
interest, and receive image
tags. Specifically, Image Capture Screen 210 includes a Shutter Button 220
configured to take a picture.
Once the picture is taken it is optionally automatically sent via Network 115
to Image Processing System
110 for tagging. Image Capture Screen 210 optionally further includes a
Rectangle 230 configured to
highlight a point of interest within the image. Rectangle 230 is controllable
(e.g., movable) by selecting
and/or dragging on the screen using a user input device. On a typical
smartphone this user input device
may include a touch screen responsive to a finger touch. As described
elsewhere herein, the
point/region of interest may be provided to Image Processing System 110 in
association with an image
to be tagged.
[00103] Image Capture Screen 210 further includes a Field 240 showing a
previously captured image and
resulting image tags. In the example, show the previously captured image
includes the same white cup
without the Rectangle 230 and the image tags include "White Starbucks Coffee
Cup." Also shown is text
stating "Slide for options."
[00104] FIG. 3 illustrates search results based on an image analysis,
according to various embodiments of
the invention. These results are optionally displayed automatically or in
response to selecting the "Slide
for options" input shown in FIG. 2. They may be generated by automatically
executing an internet
search on the image tags. Illustrated in FIG. 3 are a Sponsored Advertisement
310, Related Images 320
and other search results 330. The search results are optionally generated
using Advertising System 180
and image tags generated using Image Processing System 110. A user may of the
option of reviewing
previously tagged images. This history can be stored on Image Source 120A or
in Memory 135.
[00105] FIG. 4 illustrates methods of processing an image, according to
various embodiments of the
invention. In these methods an image is received. The image is provided to
both Automatic
Identification System 152 and at least one of Destinations 125. As a result,
both computer generated
and manual image reviews are produced. The methods illustrated in FIG. 4 are
optionally performed
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,
,
using embodiments of the system illustrated in FIG. 1. The method steps
illustrated in FIGs. 4-8 may be
performed in a variety of alternative orders.
[00106] In a Receive Image Step 410 and image is received by Image Processing
System 110. The image
is optionally received from one of Image Sources 120 via Network 115. The
image may be in a standard
format such as TIF, JPG, PNG, GIF, etc. The image may be one of a sequence of
images that form an
image sequence of a video. The image may have been captured by a user using a
camera. The image
may have been captured by a user from a movie or television show. In some
embodiments Receive
Image Step 410 includes a user using an image capture application to capture
the image and
communicate the image to Image Processing System 110. This application may be
disposed within a
camera, television, video display device, multimedia device, and/or the like.
Receive Image Step 410 is
optionally facilitate using Content Processing Logic 185.
[00107] In one illustrative example, the image is received from image
sequence, e.g. a video. The video
is displayed on a monitor, television, goggle, glasses, or other display
device. The video is optionally
received via a video streaming service such as youtube.com or Netflix.com
and/or displayed within a
browser. Logic within the display system (e.g., Image Marking Logic 147 within
Image Source 120A) is
configured for a user to indicate a particular subset of the images within the
video. The same logic may
be configured to receive an advertisement selected in response to image tags
generated from the image
and to display the advertisement over or at the same time as the video.
Selection of advertisements
based on image tags is discussed further elsewhere herein.
[00108] Specifically, using this system, a user may select an object within a
video or movie for tagging
and in response optionally receive tags characterizing that object. The user
may also or alternatively
receive an advertisement selected based on the tags. The advertisement may be
displayed in real-time
in conjunction with the video (e.g., as an overlay or added video sequence) or
provided to the user via
other communication channels (e.g., e-mail). In one illustrative example, a
user sees an object within a
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video that they like. They select the object and this selection is received in
Receive Image Step 410. In
response they receive an advertisement related to the object. The
advertisement is displayed as an
overlay, bar or caption on the video in real-time as the video is viewed on
the display. The
advertisement is optionally interactive in that it includes a link to make a
purchase.
[00109] In some embodiments, objects within an image may include particular
characteristics configured
to assist in identifying the object. For example, a particular pattern of data
bits may be encoded within
the image or within object of the image. These data bits may encode for an
image tag.
[00110] In an optional Receive Subset Identification Step 415, data
identifying one or more subsets of
the image is received by Image Processing System 110. Typically, the one or
more subsets include a set
of image pixels in which an item of particular interest is located. The one or
more subsets may be
identified by pixel locations, screen coordinates, areas, and/or points on the
received image. In some
embodiments, the subsets are selected by a user using a touch screen or cursor
of one of Image Sources
120.
[00111] In an optional Receive Source Data Step 420, source data regarding the
source of the image,
received in Receive Image Step 410, is received by Image Processing System
110. As discussed
elsewhere herein, the source data can include geographic information, an
internet protocol address, a
universal resource locator, an account name, an identifier of a smartphone,
information about a
language used on a member of Image Sources 120, a search request, user account
information, and/or
the like. In some embodiments, source data is automatically sent by an
application/agent running on
Image Source 120. For example, global positioning system coordinates may
automatically be generated
on a smartphone and provided to Image Processing System 100.
[00112] In an optional Receive Analysis Priority Step 425 a priority for the
tagging of the image, received
in Receive Image Step 410, is received within Image Processing System 110. In
some embodiments, the
priority is manually entered by a user of Image Source 120A. In some
embodiments, the priority is
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dependent on an amount paid for the review of the image. In some embodiments,
the priority is
dependent on a type of Image Sources 120A. For example, images received from a
static website may
automatically be given a lower priority relative to images received from a
handheld mobile device. An
image whose source is identified by a universal resource locator may be given
a lower priority relative to
images whose source is identified by a mobile telephone number. As such, the
priority is optionally
derived from the source data received in Receive Source Data Step 420.
[00113]The image and data received in Steps 410-425 are optionally received
together and optionally
stored in Memory 135.
[00114] In a Distribute Image Step 430, the image, and optionally any
associated data received in Steps
415-425, is distributed to Automatic Identification System 152 via Automatic
Identification Interface
150. This distribution may be internal to Image Processing System 110 or via
Network 115.
[00115] In a Receive Automated Response Step 435, a computer generated image
review is received
from Automatic Identification System 152. The computer generated image review
includes one or more
image tags assigned to the image by Automatic Identification System 152. The
computer generated
image review also includes a measure of confidence. The measure of confidence
is a measure of
confidence that the image tags assigned to the image correctly characterize
contents of the image. For
example, an image including primarily easily recognizable characters may
receive a higher measure of
confidence relative to an image of abstract shapes.
[00116] In an Optional Determine Confidence Step 440, the measure of
confidence included in the image
review is compared with one or more predetermined levels. The predetermined
levels are optionally a
function of the priority of the image review, a price of the image review, a
source of the image, and/or
the like. In an Optional Confident? Step 445 the process proceeds to an
optional Perform Search Step
450 if the confidence of the computer generated image review is above the
predetermined threshold(s)
and proceeds to a Queue Image Step 460 if the confidence of the computer
generated image is below
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,
,
,
the predetermined threshold(s). Determine Confidence Step 440 is optionally
performed using Review
Logic 170.
[00117] In Perform Search Step 450, the image tags assigned to an image are
used to perform a search.
For example, the image tag "Ford car" may be used to automatically perform a
google search using the
words "Ford" and "car."
[00118] In a Provide Results Step 455, the image tags assigned to the image
and optionally the results of
a search performed in Perform Search Step 450 are provided to a requester of
the image review. For
example, if the image was received from Image Source 120A and Image Source
120A is a smartphone,
then the image tags and search results are typically provided to the
smartphone. If the image was
received from a member of Image Sources 120, such as a website, that the image
tags and optional
search results may be provided to a host of the website, to a third party, to
Advertising System 180,
and/or the like. In some embodiments, the image tags are automatically added
to the website such that
the image tags are searchable, e.g., can be searched on to find the reviewed
image.
[00119]In Queue Image Step 460, the image is placed in Image Queue 145. This
placement optionally
includes marking a subset of the image using Image Marking Logic 147. As
described elsewhere herein,
the marking is typically configured to identify objects of particular interest
in the image. Advancement
of the image in Image Queue 145 may be dependent on the image's review
priority, the source of the
image, available human image reviewers, the measure of confidence of the
computer generated review
of the image, and/or the like.
[00120] In a Determine Destination Step 465 one or more members of
Destinations 125 are determined
for the manual review of the image. The determination of a destination is
optionally based on image
tags included in a computer generated image review received from Automatic
Identification System 152;
optionally based on specialties of human image reviewers at different
Destinations 120; optionally
based on review scores of these human image reviewers, and/or based on other
criteria discussed
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herein. In some embodiments, Determine Destination Step 465 is based on the
data characterizing the
image and a specialty of the human reviewer. The data characterizing the image
can be image features,
image descriptors, and/or information derived therefrom. As is discussed
elsewhere herein, the image
features and/or image descriptors are optionally received along with the image
from a member of
Image Sources 120. Information derived therefrom may be generated at the
member of Image Sources
120, at Image Processing System 110 and/or at Automatic Identification System
152.
[00121] In a Post Image Step 470, the image is posted to at least one of the
Destinations 125 determined
in Determine Destination Step 465. In some embodiments, Post Image Step 470
includes posting the
image to more than one of Destinations 125 in parallel. The image is
optionally posted via Network 115
and is optionally posted along with a mark highlighting a subset of the image,
source data for the image,
a time before review expiration for the image, image tags for the image
received from Automatic
Identification System 152, and/or the like.
[00122] In a Receive Review Step 475, a manual review of the image is received
from one or more of the
determined Destination(s) 125. The manual image review may include one or more
image tags assigned
to the image by a human image reviewer. The one or more image tags are
representative of the content
of the image. The manual review may also include an upgrade request, an
indication that the image is
unreviewable, an indication that the image is improper, an indication that the
review expired, and/or
the like.
[00123] In an Image Tagged? Step 480 the progress of the method is dependent
on whether image tags
were received in Receive Review Step 475. If image tags characterizing the
content of the image were
received then the method optionally proceeds with Perform Search Step 450 and
Provide Results Step
455. In these steps the image tags included in the manual image review and
optionally the computer
generated image review are used. Use of the image tags in the computer
generated image review may
be dependent on the confidence measure of this review.
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100124] Steps 460-475 are optional if in Step 445 the confidence measure is
found to be above the
predetermined threshold(s).
[001251In an optional Upgrade? Step 485 the progress of the method is
dependent on whether an
upgrade request has been received. If such a request has been received then
the method proceeds to
Determine Destination Step 465 wherein a second/different member of
Destinations 125 is determined.
The determination may depend on image tags received in the manual image review
received in Receive
Review Step 475. The upgrade request may be received from a human image
reviewer or from a
requester of the image review (from Image Source 120A or 12013, etc.). The
upgrade request may be
received after the requestor has had a chance to review the image tags
provided in Provide Results Step
455. For example, the requestor may first receive image tags consisting of
"white car" and then request
a review upgrade because they desire further information. The review upgrade
may result in the image
being provided to a human image reviewer with a specialty in automobiles. This
human image review
can add to the existing image tags to produce "white car, 1976 Ford Granada."
In some embodiments,
the requester can add source data indicating a subset of the image when
requesting a review upgrade.
For example, the reviewer may wish to indicate particular interest in a broken
headlight. This serves to
direct the human image reviewers attention to this feature of the image,
produce tags that include
"broken headlight," and result in a search (Perform Search Step 450), directed
toward broken headlights
for a 1976 Ford Granada.
[00126]In some embodiments, upgrade request are generate automatically by
Review Logic 170. For
example if an image review appears too brief, e.g., just "car," then Review
Lotic 170 may automatically
initiate a review upgrade. In some embodiments, the automatic generation of
upgrade requests is
based on the presence of keywords within a manual image review. For example,
certain review
specialties are associated with lists of keywords. In some embodiments, when
one of these keywords
are received in a manual image review and an automated review upgrade is
initiated. The review
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upgrade preferably includes a human image reviewer having a specialty
associated with the received
keyword. In a specific example, one specialty includes "automobiles" and is
associated with the
keywords "car," "truck," "van," "convertible," and "Ford." When one of these
keywords is received in a
manual image review, Review Logic 170 checks with Review Logic 157 to
determine if a human image
reviewer having a specialty in "automobiles" is currently active. If so, then
an automatic upgrade is
initiated and the image is sent to the Destination 125B of the reviewer having
the "automobiles"
specialty.
[00127] If no upgrade requests are made, then in an End Step 490, the process
is completed.
[00128] FIG. 5 illustrates alternative methods of processing an image,
according to various embodiments
of the invention. In these methods, at least some of Steps 430-445 are
performed in parallel with at
least some of Steps 460-475. The manual image review is in Steps 460-475 may
be begun before the
computer generated review of Steps 430-445 is complete, thus, the manual image
review is started
before the confidence measure of the computer generated review is known. If,
in Confident? Step 445,
the confidence measure is found to be above the predetermined threshold(s),
then Steps 460-475 are
optionally aborted.
[00129] FIG. 6 illustrates methods of managing a reviewer pool, according to
various embodiments of
the invention. In this method the status of a reviewer may be changed based on
their performance in
reviewing images. The steps illustrated may be part of and performed in
concert with the methods
illustrated by FIGs. 4 and 5. For example, they may be performed in part
between Receive Image Step
410 and Receive Review Step 475. The methods illustrated include sending an
image to more than one
of Destinations 125.
[00130] In Receive Image Step 410 an image is received. As is discussed
elsewhere herein, the image
may be received at Image Processing system 110 via Network 115. The image may
be generated by a
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camera and/or obtained from a webpage. In some embodiments, the image is
received along with
information about how often the web page is viewed.
[00131] In a Select 1st destination Step 610 a first destination is selected
for manual or automated
analysis of the image. Select 1st Destination Step 610 is performed using
Destination Logic 160 and is an
embodiment of Determine Destination Step 465. As described elsewhere herein,
the determination of a
destination for the image may be based on a wide variety of factors, including
the status of a human
reviewer and scores associated with reviewers. For example, typically a member
of Destinations 125
associated with an active reviewer will be selected, rather than one without
an active reviewer. The
selected destination may be a member of Destinations 125 and/or Automatic
Identification System 152.
[00132]In Post Image Step 470, the image received in Receive Image Step 410 is
posted to the selected
member of Destinations 125. As discussed elsewhere herein, posting of the
image can include
communicating the image via Network 115 using standard network protocols such
as TCP or UDP.
[00133] In an optional Monitor Step 620, Reviewer Logic 170 is used to monitor
progress of a manual
image review of the image at the member of Destinations 125 selected in Select
1st Destination Step
610. The monitoring can include detection of input by a human reviewer, time
taken for the image
review, a number of words provided that characterize the image, and/or the
like. Monitoring optionally
includes measuring a time to taken to tag the image. Where monitoring includes
detection of input by a
human reviewer, the monitoring can be on a keystroke-by-keystroke basis, on a
word-by-word basis
and/or on a line-by-line basis. As such, Reviewer Logic 170 may be configured
to receive data
characterizing the image a character, word or line at a time.
100134] In Remove Step 630, the image is removed from processing at the member
of Destinations 125
Selected in Select 1st Destination Step 610. "Removal" can include, notifying
the human reviewer at the
selected member of Destinations 125 that he or she is no longer primarily
responsible for reviewing the
image, relieving the human reviewer of primary responsibility (without
necessarily notifying the human
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reviewer), removing the image from a display of the human reviewer, and/or the
like. In some
embodiments, Remove Step 630 includes merely placing a human reviewer in a
ranking to have
secondary or shared responsibility for reviewing an image. For example, if the
human reviewer
associated with the member of Destinations 125 selected in Select 1st
Destination Step 610 had primary
responsibility for reviewing an image, the responsibility may now be shared or
assigned to other
reviewers associated with other members of Destinations 125. In this case it
is the primary
responsibility that is "removed."
[00135] Remove Step 630 may occur if manual review of the image is taking too
long. For example, if in
Monitor Step 620 it is found that the reviewer has not started typing after a
predetermined time, then
Remove Step 630 may be performed. Other examples, of triggering events for
Remove Step 630
include, loss of communication with the selected member of Destinations 125,
exceeding a
predetermined time allotment for review of the image, improper or
inappropriate image tags received
from the human reviewer, inaccurate (not characterizing the image) image tags
received from the
human reviewer, a referral from a first human reviewer to a second human
reviewer, an upgrade
request of the image review, and/or the like.
[00136] In a Select 2nd Destination Step 640 a second member of Destinations
125 (or Automatic
Identification System 152) is selected using Destination Logic 160. The second
member may be selected
based on any of the criteria discussed above with regard to Select 1st
Destination Step 610 and
Determine Destination Step 465. Further, in some embodiments the selection of
a second member may
be based on a specific referral by a human reviewer associated with the first
member of Destinations
125. For example, a first human reviewer may identify the content of an image
to be a specialty of a
second human reviewer and may refer the image to the member of Destinations
125 associated with
the second human reviewer. The selection of a second member of Destinations
125 in Select 2nd
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Destination Step 640 is optionally dependent on automated processing on an
image using Automatic
Identification System 152.
[00137] In another Post Image Step 470 the image is posted to the member of
Destinations 125 Selected
in Select 2nd Destination Step 640. In some embodiments, more than one human
reviewer may review
an image in parallel. They may perform the review independently or in
cooperation. One reviewer may
have primary responsibility for review of the image or each reviewer may have
equal responsibility. One
reviewer may have supervisory responsibility over one or more other reviewers.
In some embodiments,
Select 2nd Destination step 640 is performed and the image is posted to two or
more members of
Destinations 125 prior to Monitor Step 620 and/or Remove Step 630.
[00138] In Receive Review Step 475 a review of the image, e.g., image tags, is
received as discussed
elsewhere herein. The review typically includes image tags characterizing
contents of the image.
Reviews may be received from more than one of Destinations 125. For example,
tags characterizing an
image may be received from the members of Destinations 125 selected in both
Select rt Destination
Step 610 and Select 2nd Destination Step 640. Receive Review Step 475 is
optionally performed in real-
time as characters or words are provided by human reviewer(s).
[00139] In an optional Associate Tags Step 650, one or more image tags
characterizing the image are
stored in association with the image. The stored tags optionally include tags
provided by more than one
human reviewer and may be stored in Memory 135. As described elsewhere herein,
the tags may
further be provided to a member of Image Sources 120 (e.g., in an embodiment
of Provide Results Step
455) or used to select advertisements using Advertising System 180. The tags
may also be provided to
Automatic Identification System 152 to provide training of automatic image
recognition processes.
[00140] FIG. 7 illustrates methods of receiving image tags in real-time,
according to various
embodiments of the invention. These methods are optionally performed by Image
Processing System
110 and the image tags may be a result of a manual image review. These methods
may be performed in
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,
concert with the other methods described herein, for example as part of the
methods illustrated by FIG.
4. The methods begin with Post Image Step 470 in which an image is provided to
one or more members
of Destinations 125, as discussed elsewhere herein. The methods illustrated in
FIG. 7 are optionally
preceded by Receive Image Step 410 in which an image is received from a remote
computing device.
[00141]In a Receive Input Step 710 input is received from the one or more
members of Destinations
125. This input typically includes characters provided by a human reviewer.
For example, the input may
be characters typed by a human reviewer at Destination 125A. Typically,
Receive Image Step 710 is
continued as other steps shown in FIG. 7 are performed.
[00142]In a Detect 1st Word Step 720 a word is detected in the input received
in Receive Input Step 710.
The word may be detected by the presence of a whitespace character such as an
ASCII space or carriage
return. Spell checking is optionally performed on the detected word. If the
word is not include in a
spellcheck dictionary, then an attempt at correction may be made or the human
reviewer may be
notified of the failure to recognize the word.
[00143]Detection of the word in Detect 1st Word Step 720 results in execution
of a Deliver 1st Word Step
730 in which the word is communicated to a source of the image. For example,
once a word is detected
it may be provided to Image Source 120A in real-time. At Image Source 120A the
word can be displayed
to a user. Displaying one word at a time can provide an impression that the
analysis of the image is
occurring in a shorter amount of time, as compared to waiting until an entire
set of image tags are
received before displaying the set.
[00144]In a Detect 2nd Word Step 740 a second word is detected in the input
received in Receive Input
Step 710. Again, the word may be detected by the presence of a whitespace
character and can occur
after providing the first word to the user at Image Source 120A. Both the
first and second words are
expected to be tags characterizing the image. Detection of the second word in
Detect 2nd Word Step
740 triggers a Deliver 2'd Word Step 750 in which the second word is delivered
to the image source, e.g.,
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Image Source 120A. Detect 2nd Word Step 740 and Deliver 2nd Word 750 may be
repeated for third,
fourth and additional word, each being part of the image tags.
[00145] In a Detect Completion Step 760 data indicating that processing of the
image is completed, e.g.,
that the words detected comprise all the words (image tags) to be provided by
the human reviewer are
received. The data may include a metadata tag such as Yendtags," an ASCII
carriage return, and/or the
like. Typically, Detect Completion Step 760 occurs after one, two or more
image tags have been
received. In optional Associate Tags Step 650 the received image tags are
associated and/or stored with
the image as discussed elsewhere herein.
[00146] While FIG. 7 illustrates detection and delivery of a word at a time,
in alternative embodiments,
individual keystrokes are detected and delivered. Receive Input Step 710 may
continue in parallel with
Steps 720-740 and/or 750. Steps 710-760 may be include as part of Receive
Review Step 475, discussed
elsewhere herein.
[00147] FIG. 8 illustrates methods of upgrading an image review, according to
various embodiments of
the invention. In these methods an image receives more than one phase of image
review. Following a
first review (phase 1) the image review is upgraded and reviewed further
(phase 2). The request for
upgrade can be automatically generated, initiated by a first human reviewer,
and/or be in response to a
request from a source of the image. Both the first and second review may be
manual, i.e., performed by
a human reviewer. Alternatively, the first review may be automatic and one or
more subsequent
reviews may be manual, or the first review may be manual and one or more
subsequent reviews
automatic.
[00148]In Receive Image Step 410 an image is received. A first member of
Destinations 125 is selected
for the image in Select 1st Destination Step 610. The image is then posted in
Post Image Step 470. These
steps are discussed elsewhere herein.
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[00149] In a Receive 1st Review Step 810 a first review of the image is
received. This first review may
include one or more image tags characterizing the contents of the image. For
example, the image
review may include words "black spider" in response to a picture including an
image of a black spider; or
the image may include the words "red car" in response to an image including a
red automobile. Receive
1st Review Step is optionally an embodiment of Receive Review Step 475, and
may include the real-time
communication of image tags as discussed in regard to FIG. 7.
[00150] In some embodiments, the first review can include an indication,
provided by the human
reviewer that performed the first image review, that the processing of the
image should be upgraded.
For example, the first human reviewer may manually indicate a field of
expertise for a second (optionally
specialized) human reviewer. For example, a first human reviewer may provide
the "red car" image tags
and suggest an upgraded review be performed by a reviewer with automotive
expertise. Alternatively,
the first review can include image tags that are considered particularly
valuable. For example, an
automatic review that indicates a 72% probability that the image includes a
wedding dress may trigger
an automated upgrade to a manual review because the image tags "wedding dress"
are potentially of
greater commercial value than other image tags. In some embodiments, this
automated upgrade is
performed by Review Logic 170 and is based on a list if relatively important
or valuable keywords stored
in Memory 135. This list can include keywords and an associated measure of
their value. As discussed
elsewhere herein, automatic upgrades performed by Review Logic 170 are
optionally based on image
tags automatically generated using Automatic identification System 152 and/or
information predictive
of how often an image will be viewed. These factors are optionally applied
using an algorithm that
maximizes the potential value of tagging the image using a human reviewer and
providing
advertisements based on these tags. Examples of more valuable image tags may
be related to shoes,
cars, jewelry, travel destinations, books, games, clothing, holidays, food,
drink, real estate, banks,
accidents, etc.
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[00151] In some embodiments, upgrades of image reviews are automatic. For
example, a tag of "black
spider" may automatically result in an upgrade of the image review that
includes sending the image to a
human reviewer having a particular specialty, e.g., a spider expert. The
identification of particular plant
or animal life often includes (depends on) location information, as the
location of the plant or animal
can be important for proper identification.
[00152] In some embodiments, as discussed elsewhere herein, upgraded reviews
may be requested by
the person who originally requested that the image be reviewed. For example, a
user of Image Source
120A may provide an image of a dog and received image tags comprising "black
dog." The user may
then request further detail by providing the word "breed?" In this case the
image review may be
upgraded and sent to a human reviewer with specific knowledge of dog breeds.
In some embodiments,
the user is charged for the upgrade or is required to have a premium account
in order to request
upgrades. The user may specify a particular part of an image when requesting
an image review upgrade.
[00153] The presence of an upgrade request (automatically and/or manually
generated) is detected in a
Detect Upgrade Request Step 820. The detection may be based on data or a
command received from a
member of Image Sources 120, from a member of Destinations 125, Automatic
Identification Interface
150, and/or from a component of Image Processing System 110 such as Review
Logic 170, Content
Processing Logic 185 or Response Logic 175.
[00154] In a Select 2nd Destination Step 640, Destination Logic 160 is used to
select a second member of
Destinations 125 and/or Automated Identification System 152 for review of the
image. This selection
can be based on any of the criteria on which Select rt Destination Step 610
was based and, in addition,
the image tags and/or other information resulting from the first review. For
example, the selection of a
second member of Destinations 125 may be based, at least in part, on an image
tag manually or
automatically generated in the first image review. Specifically, a tag of
"black spider" may be used by
Destination Logic 160 to select a member of Destinations 125 associated with a
human reviewer having
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'
=
expertise in the identification of spiders. In another example, selection of a
second member of
Destinations 125 may be based on a word provided by a user requesting the
image review. Specifically,
if the first review produces image tags "white shoe" and the user responds
with "brand?" then
Destination Logic 160 may use this information to select a member of
Destinations 125 associated with a
human reviewer having expertise in shoe brands.
100155]In some embodiments of Select 2nd Destination Step 640, Destination
Logic 160 is configured to
possibly select Automatic Identification System 152 for the second review of
the image, rather than a
member of Destinations 125. This may occur, for example, when the image has
been tagged with the
name of an actor and the upgrade request requests "movie name?" In such a
case, the image may be
searched for in a library of move images. The same approach may be taken for
other reproducible
objects such as currency, paintings, car models, trademarks, barcodes, QR
codes, well known persons,
etc.
[00156]In another instance of Post Image Step 470 the image is posted to the
second selected member
of Destinations 125 or Automatic Identification System 152 for a second review
of the image. In a
Receive 2nd Review Step 830, image tags characterizing the content of the
image are typically received.
Receive 2nd Review Step 830 is optionally an embodiment of Receive Review Step
475. Alternatively, an
additional referral, indication that the image cannot be tagged for some
reason or other information
may be received. The image tags are received from the member of Destinations
125 or Automatic
_
Identification System 152 to which the image was posted. Steps 820, 640, 470
and 830 may be repeated
if needed.
100157] In Associate Tags Step 650 received image tags are associated with the
image and/or provided
to the source of the image, as discussed elsewhere herein.
[00158] In one illustrative example of the methods illustrated by FIG. 8 an
image is received from a web
page. The image is sent to Automatic Identification System 152 for automated
review. The result of the
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automated review includes an image tag "ring." This tag is processed using
Review Logic 170 and
identified as a potentially valuable image for use in advertising. As
discussed elsewhere herein, this
identification is optionally also based on other factors such as how often the
image is viewed on the web
page. As a result of the identification, the review of the image is
automatically upgraded and sent to a
member of Destinations 125 that is associated with a human reviewer having
expertise in Jewelry. The
human reviewer modifies the image tags to include "gold wedding ring" and
these tags are associated
with the image. These image tags may then be used to select advertisements
using the systems and
methods described elsewhere herein.
[00159] In one illustrative example of the method illustrated by FIG. 8 an
image is received from an
application executing on a mobile device. The image includes a scene of a
street and is sent to
Destination 125A for review by a human reviewer. The human reviewer response
with image tags
"street scene" and these tags are provided to the mobile device. A request for
a review upgrade is then
received from the mobile device. This request includes the text "car model?"
and an identification of
part of the image including a car. As a result of this request the image, text
and identification are sent to
Destination 125A or another member of Destinations 125 for further manual
review. The further review
results in the image tags "1909 Model-T" which are then forwarded to the
mobile device.
[00160] In one illustrative example of the method illustrated by FIG. 8 an
image is received from a
computing device. The image includes several pieces of paper currency laying
on a plate and is sent to
Destination 125A for manual review. The resulting tags include "US currency on
white plate" and are
sent to the computing device. A request for an image review upgrade is
received. The request includes
"how much?" As a result of this request the image is sent to Automatic
Identification System 152 where
automated currency identification logic is used to identify the amounts of the
currency and optionally
provides a sum. This information is then sent back to the computing device.
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[00161] In one illustrative example of the method illustrated by FIG. 8 an
image is received from a
mobile device. The image includes a leaf of a plant and is sent to Destination
125A. The human
reviewer at Destination 125A provides the image tags "green leaf" and also
upgrades the image review.
As a result of the upgrade and the image tags the image is sent to Destination
1258, which is associated
with a second human image reviewer having expertise in botany. The selection
of Destination 1258 is
based, in part on the image tags "green leaf." In parallel, the image tags
"green leaf" are sent to the
mobile device. At Destination 1258 the second human image reviewer adds the
words (tags) "poison
ivy" to the already existing image tags. These additional tags are then also
sent to the mobile device.
On the mobile device the words "green leaf" are first displayed and then the
words "poison ivy" are
added to the display once available.
[00162]The methods illustrated by FIG. 8 are optionally used in concert with
other methods described
herein. For example, the image tags may be used in Auction Tag Step 1560
described elsewhere herein.
The tags may be used to select an advertisement and the advertisement provided
to a remote browser
for display on a webpage along with the image.
[00163] FIG. 9 illustrates an example of Image Source 120A including
electronic glasses, according to
various embodiments of the invention. Electronic glasses include glasses to be
worn by a person.
Examples include "Google Glass" by Google , "M100 Smart Glasses" by Vuzix ,
the iOptikTM contact lens
by Innovega, and/or the like. These systems are configured to allow a user to
view both the real world
and an electronic display at the same time. Electronic glasses may also
include virtual reality systems
such as the Oculus RiftTM by Oculus VR. These types of systems display images
to a user using an
electronic display, but do not provide simultaneous direct view of the real
world. A direct view is a view
that is not digitized for viewing, e.g., a view through a glass or lens.
[00164] Generally, electronic glasses provide in interactive interface in
which a user can select a subset
of the image in real-time as the image is being viewed in or through the
electronic glasses. As used
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,
herein, "real-time" selection is meant to mean that the image is being viewed
as it is being captured,
with only inconsequential delay. For example, an image viewed in real-time may
be captured by a
camera and processed using a graphics engine and displayed with only a delay
resulting from electronic
processing times. Real-time viewing allows the user to position objects of
interest within the viewed
image by moving the image capture device as the image is being viewed. Thus,
real time viewing
excludes viewing of images that have been stored for substantial periods
before viewing.
[00165]As illustrated in FIG. 9, Image Source 120A includes a Camera 910
configured to capture images.
The captured images can include still images or video comprising a sequence of
images. A Display 920 is
configured to present the captured images to a user of Camera 910. In some
embodiments, such as
those in which Image Source 120A is a smart phone, Display 920 includes a
touch screen configured to
function as a view finder for Camera 910, to display captured images and to
display Image Capture
Screen 210 (described elsewhere herein). Display Logic 925 is configured to
manage the display of
images and other content on Display 920. Display Logic 925 can include
hardware, firmware and or
software stored on a computer readable medium
[00166]In the embodiments illustrated by FIG. 9, Image Source 120A further
comprises Selection Logic
930 configured for the user of Image Source 120 to indicate a subset of a
captured image. This
indication can be made within Image Capture Screen 210 in real-time as the
image is being displayed
and/or captured on Display 920. As discussed elsewhere herein, such an
indication is made made to
select a particular object of interest within the image. Following selection,
the subset of the image is
optionally marked using Image Marking Logic 147 as discussed elsewhere herein.
Image Marking Logic
147 may be used add a mark to the image as shown within Display 920. As such,
the user can see the
location that has been marked. Selection Logic 930 is optionally configured
for the user to remark the
subset of the image until the user is satisfied with the selection.
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100167] In some embodiments, Selection Logic 930 includes Tracking Logic 935
configured to track
movement of the user's eyes. Tracking Logic 935 is optionally included within
electronic glasses. Eye
tracking can include detection of the focal point of both eyes, the direction
of one or more eyes (eyeball
direction), the focus of one or more eyes, blinking, eyeball movement, and/or
the like. Tracking Logic
935 is optionally configured to correlate a state of the user's eyes with a
location within captured
images. Geometry data representing a geometric relation between Camera 910 and
the physical
elements of Selection Logic 930 are used to associate the state of the user's
eyes with the location
within an image captured using Camera 910.
[00168]Tracking Logic 935 optionally includes a second camera directed at the
eyes of the user. This
camera may be mounted on electronic glasses or be part of other embodiments of
Image Sources 120.
For example, Tracking Logic 935 configured for tracking a user's eyes may be
included in a web camera,
a smartphone, a computer monitor, a television, a tablet computer, and/or the
like.
[00169] In some embodiments, Tracking Logic 935 is configured to detect
blinking of one or more eyes.
For example, Tracking logic 935 may be configured to detect a single eye blink
or a pattern of eye blinks.
When such an event is detected, Selection Logic 930 may select a position
within an image based on eye
position data received from Tracking Logic 935, or alternatively select a
position at the center of the
currently viewed image.
100170]Once an image has been marked using Marking Logic 147 and Selection
Logic 930, the location
and/or area of the marking can be displayed to the user on the marked image
within Display 920. For
example, the image, plus a red "X" at the marked location, may be displayed to
the user within Image
Capture Screen 210. In some embodiments, the user may then confirm the
selection using Confirmation
Logic 940. Confirmation Logic 940 is optionally responsive to Tracking Logic
935. For example,
confirmation may be provided using a blink or other eye movement, an audio
command, a verbal
command, or a touch command. In some embodiments, Tracking Logic 935 is
configured to detect, and
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interpret as a command, movement of the eyes into an unnatural position (e.g.,
cross-eyed). Such a
movement can be used to provide a confirmation command. Confirmation is
optionally required prior
to sending the image to Network 115.
[00171] In some embodiments, Selection Logic 930 includes Tracking Logic 935
that is configured to
track something other than or in addition to the eyes. For example, Tracking
Logic 335 may be
configured to detect a pointing finger of a user, an electronic device worn on
a finger or wrist, and or the
like. In these embodiments, Selection Logic 930 is configured to infer a
location within an image based
on the detected object. In one embodiment, Tracking Logic 935 is configured to
detect the location of a
pointing finger within an image and infer that the location to be selected is
at the tip of the finger. A
user can point to an object within their field of view, provide an audio, eye
based, and/or touch based
command to Image Source 120A, and the position of the pointing finger will be
used to make the
selection of a position within the image. In one embodiment, Tracking Logic
935 is configured to detect
the location of a wireless electronic device relative to Image Source 120A and
infer that the location to
be selected is along a line between the wireless electronic device and a part
of Image Source 120A.
[00172] Image Source 120A further includes an I/O 945 configured for Image
Source 120A to
communicate to Image Processing System 110 via Network 115. I/O 945 can
include wired and/or
wireless connections. For example, in some embodiments, I/O is configured to
communicate wirelessly
from electronic glasses to a cellular phone using a BluetoothTM connection and
then for the
communication to be forwarded from the cellular phone to Network 115 using
Wifi or a cellular service.
[00173] Image Source 120 further includes an embodiment of Memory 135
configured to store images
captured using Camera 910, geometric data, account data, and/or the like.
Memory 135 includes non-
transient memory such as Random Access Memory (RAM) or Read Only Memory (ROM).
Memory 135
typically includes data structures configured to store captured images and
marking locations within
these images.
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[00174] Image Source 120 further includes a Processor 950. Processor 950 is a
digital processor
configured to execute computing instructions. For example, in some embodiments
Processor 950 is
encoded with computing instructions to execute Display Logic 925, Selection
Logic 930, Image Marking
Logic 147 and/or Tracking Logic 935. Processor 950 optionally includes an
Application Specific Integrated
Circuit (ASIC) or Programmable Logic Array. (PLA).
[00175] Image Source 120 optionally further includes Object Tracking Logic
955. Object Tracking Logic
955 is configured to track movement of an object of interest within a sequence
of images. For example,
in some embodiments, a user may use Selection Logic 930 to select a subset of
an image or an aspect of
the image for which information is requested. This subset may include one or
more pixels. Object
Tracking logic 955 is configured to use automatic (computer based) image
interpretation logic to identify
a specific object occupying the selected subset. This object may be a person,
a vehicle, an animal, or any
other object. The boundaries or other pixels of the selected object are
optionally highlighted in Display
920 by Object Tracking Logic 955. This highlight can track the object as it
moves within the sequence of
images and can include changing pixel characteristics. The highlighting
optionally moves with the object
on the display. An aspect of the image may be a brand of an object within the
image, a movie from
which the image is obtained, a location of the content of an image, etc. In
some embodiments, aspects
of the images can be specified as being of interest using text such as "shoe
brand?," "movie?," "actor?,"
"location," "breed?," etc. Such specifications may be provided in an original
request to tag an image
and/or in an upgrade request.
[00176]In some embodiments, images communicated from Image Sources 120 to
Image Processing
System 100 are part of a sequence of images that comprise a short video
sequence. These video
sequences may be tagged using the systems and methods described elsewhere
herein. One advantage
of tagging a video sequence is that the tag(s) may characterize a specific
action that occurs in the video.
For example, tags of a figure skater may characterize specific jumps (double
Lutz, etc.) that are better
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identified in video than in a still image. Various embodiments include a
specific limit on the length of
the image sequence, e.g., the video must be no more than 3, 5, 7 or 10
seconds.
[001771 While the embodiments illustrated by FIG. 9 include electronic
glasses, these embodiments may
be adapted to any device having eye tracking technology including cell phones,
video display monitors
(e.g., computers or television screens), tablet computers, advertising
displays, etc. For example, the
embodiments of Image Source 120A illustrated in FIG. 9 include a television
having an eye tracking
camera configured to determine at which part of a television screen a user is
looking.
[00178] Image Source 120A optionally further includes Image Processing Logic
960 configured to
perform one or more steps for the purpose of tagging an image. Image
Processing Logic 960 is
optionally configured to reduce the load on Image Processing System 110 by
performing these one or
more steps locally to Image Source 120A. For example, Image Processing Logic
960 may be configured
for performing initial steps in tagging of an image and then send the results
of these initial steps to
Image Processing System 110 for generation of image tags. In some embodiments,
Image Processing
Logic 960 is capable of completing the tagging process for some but not
necessarily all images. Image
Processing Logic 960 includes hardware, firmware and/or software stored on
computer readable media.
For example, some embodiments include an instance of Processor 950
specifically configured to
perform the functions of Image Processing Logic 960 discussed herein.
[00179] In some embodiments, Image Processing System 110 is configured to
provide Image Processing
Logic 960 to Image Sources 120. This is optionally via an "app store" such as
the Apple App Store.
Where applicable, Providing Image Processing Logic 960 to a member of Image
Sources 120 is an
optional step in the various methods illustrated herein. Processing Logic 960
can be provided as an
"app" or computer instructions that further includes other logic discussed
herein, for example the logic
discussed in relation to FIG. 9.
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,
,
[001801 In some embodiments, Image Processing Logic 960 is configured to
identify specific features
within an image. Feature identification includes determining if specific
points within an image are or are
not part of a feature of a given type. Types of features include, but are not
limited to, edges, corners,
blobs and ridges. Generally, a feature is an "interesting" or "useful" part of
an image, for the purpose of
identifying contents of the image. Image Processing Logic 960 may be
configured to perform one or
more of a number of different feature detection algorithms. In some
embodiments, Image Processing
Logic 960 is configured to select from among a number of different algorithms
based on available
processing power and/or contents of the image. Examples of known feature
detection algorithms
include "Canny," "Sobel," "Harris & Stephens/Plessy," "SUSAN," "Shi & Tomasi,"
"Level curve curvature,"
FAST," "Laplacian of Gaussian," "Difference of Gaussians," "Determinant of
Hessian," "MSER," "PCBR"
and Grey-level blobs." These types of algorithms are executed on a computing
device and other such
algorithms will be apparent to one of ordinary skill in the art. The results
of feature identification
include identification of a specific feature type at a specific location
within the image. This may be
encoded in a "feature descriptor" or "feature vector," etc. The results of
feature detection may also
include a value representing a confidence level at which the feature is
identified.
[001811In some embodiments, Image Processing Logic 960 is further configured
to calculate image
descriptors based on identified image features. Image descriptors are visual
features of the contents of
an image and include characteristics such as shape, color, texture and motion
(in video). Image
descriptors may be part of a specific descriptor domain, such as descriptors
related to the domains of
face recognition or currency recognition. The derivation of image descriptors
is typically based on image
features. For example, derivation of a 3-D shape descriptor may be based on
detected edge features.
Image descriptors may characterize one or more identified objects within an
image.
[00182]The particular image features and image descriptors used in a
particular embodiment are
dependent on the particular image recognition algorithms used. A large number
of image recognition
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algorithms are known in the art. In some embodiments, Image Processing Logic
960 and/or Image
Processing System 110 are configured to first attempt identification of image
features and derivation of
image descriptors of various types and then to select from among a plurality
of alternative image
processing algorithms based on the levels of confidence at which the image
descriptors are derived. For
example, if image descriptors in a facial recognition domain are derived with
a high level of confidence,
then an image processing algorithm specific to facial recognition may be
selected to generate image tags
from these image descriptors.
[00183] In those embodiments that include Image Processing Logic 960 the task
of tagging an image can
be distributed between Image Sources 120 and Image Processing System 110. How
the task is
distributed may be fixed or may be dynamic. In embodiments were the
distribution is fixed specific
steps are performed consistently on specific devices. In embodiments were the
distribution is dynamic
the distribution of steps may be responsive to, for example, communication
bandwidth, image type (still
or video) processing power on Image Source 120A, current load on Image
Processing System 110,
availability of image reviewers at Destinations 125, the confidence to which
steps are accomplished on
Image Source 120, and/or image descriptor data present on Image Source 120A.
Any combination of
these factors may be used to dynamically allocate distribution of processing
steps. For example, if the
derivation of image descriptors occurs with a low degree of confidence
(relative to a predetermined
requirement) on Image Source 120A, then the image features and/or image maybe
communicated to
Image Processing System 110 for derivation of image descriptors using more
powerful or alternative
image processing algorithms. In contrast, if the derivation of image
descriptors occurs on Image Source
120A with an adequate degree of confidence, then this step need not typically
be performed on Image
Processing System 110.
[00184] If image processing steps are successfully performed on Image Source
120A by Image Processing
Logic 960, the results of these steps and/or the image may be communicated to
Image Processing
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System 110 using I/O 945. For example, in some embodiments both an image and
image descriptors are
communicated to Image Processing System 110. The image descriptors may be used
in an attempt to
automatically tag the image or may be provided to a human image reviewer at
one or more of
Destinations 125. The image descriptors may be used to identify a descriptor
domain and this domain
then used to select a member of Destinations 125 to which the image is sent.
For example, a descriptor
domain of "vehicles" may be used to select an image review having expertise in
vehicles. The
classification of an image into a domain based on image descriptors may occur
on either Image
Processing System 110 or Image Processing Logic 960.
[00185] In some embodiments automatic tagging of an image is attempted based
on derived image
descriptors. In various embodiments, this may occur using Image Processing
Logic 960 and/or
Automatic Identification System 152. Classification optionally occurs by
comparing the image
descriptors derived from the image with a library of image descriptors
associated with different classes.
For example, an image descriptor identifying a vehicle shape may match with a
previously stored image
descriptor associated with a "vehicle" class. If the class is suitable (in
type, scope, etc.) the identification
of a class may be sufficient to automatically select a tag for the image. For
example, image descriptors
matching those of a class "child face" may be sufficient to generate the tags
"child's face."
[00186] Typically, Image Processing System 110 includes a larger library of
image descriptors associated
with different classes relative to Image Source 120A. These libraries are
optionally stored in Memory
135 of Image Processing System 110 or Image Source 120A, or Automatic
Identification System 152.
Libraries of image descriptors stored in Image Source 120A are optionally
based on images previously
processed using Image Source 120A. For example, if several images from Image
Source 120A are
identified as having descriptors and tags relating to currency, a library of
descriptors in the currency
domain/class may be stored in Memory 135 of Image Source 120A. These
descriptors may be
associated with tags such as "US $5 bill." When a new image is received having
the same set of
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descriptors, Image Processing Logic 960 is optionally configured to
automatically tag the image using the
associated tags. While the descriptor library may be received from Image
Processing System 110, or
may be developed using image tags received from Image Processing System 110,
the tagging in the
above example is not dependent on real-time communication with Image
Processing System 110.
[00187] In various embodiments, data characterizing relationships between
image descriptors and
classes and/or tags may be developed on Image Processing System 110, Image
Source 120A, Destination
125A and/or Automatic Identification System 152. Once developed the data may
be transferred to
improve and/or supplement the libraries at any of the other devices.
[00188] Will the systems illustrated show a client-server architecture, in
alternative embodiments Image
Sources 120 and Destinations 125 are connected in a peer-to-peer architecture.
In these embodiments,
any combination of the elements illustrated in Image Processing System 110 may
be included in Image
Sources 120 and/or Destinations 125. One of Image Sources 120 may perform the
image tagging and
processing tasks discussed herein on an image received from another of Image
Sources 120.
[00189] FIG. 10 illustrates a method of processing an image at least partially
on Image Source 120A,
according to various embodiments of the invention. The methods illustrated by
FIG. 10 can include a
range of different processing steps performed on Image Source 120A. For
example, those steps
involving image descriptors are optionally performed on Image Processing
System 110.
[00190] In a Receive Image Step 1010 and image is received by Image Source
120A. The image may be
received from a camera included in Image Source 120A, from Image Source 120B,
from Network 115,
from Image Processing System 110, from a wireless device, from a memory
device, and/or the like. The
received image is optionally one of a sequence of images that form a video.
[00191] In an Identify Features Step 1020, Image Processing Logic 960 is used
to identify image features
within the received image. As discussed elsewhere herein, methods of
identifying image features are
known in the art. Identify Features Step 1020 may apply one, two or more of
these methods. The
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identification of features optionally includes a confidence level reflecting
an estimated accuracy and/or
completeness of the feature identification.
[00192] In an optional Send Features Step 1030 the image features identified
in Identify Features Step
1020 are sent to Image Processing System 110. The features may be sent with or
without the associated
image and may be sent via Network 115. If Send Features Step 1030 is included
in the method, the
method optionally next proceeds to a Generate/Receive Tags Step 1070 in which
Tags for the image are
received from Image Processing System 110. Image Processing Logic 960 is
optionally configured to
perform Send Features Step 1030 based on a confidence level of the features
calculated in Identify
Features Step 1020. For example, if the confidence is below a threshold the
step may be executed and
both the image and the features sent.
[00193]In an optional Derive Descriptors Step 1040 Image processing Logic 960
is used to derive image
descriptors from the image features identify in Identify Features Step 1020.
As discussed herein, a wide
assortment of methods is known in the art for deriving image descriptors. In
some embodiments,
Derive Descriptors Step 1040 includes using more than one method. The
derivation may include a
confidence level reflecting an estimated accuracy and/or completeness of the
descriptor derivation. The
types and content of descriptors derived is typically dependent on the image
recognition algorithm(s)
used.
[00194]1n an optional Send Descriptors Step 1050 the image descriptors derived
in Derive Descriptors
Step 1040 are sent to Image Processing System 110. The image descriptors may
be sent with or without
the associated image and may be sent via Network 115. If Send Descriptors Step
1050 is included in the
method, the method optionally next proceeds to a Generate/Receive Tags Step
1070 in which Tags for
the image are received from Image Processing System 110. Image Processing
Logic 960 is optionally
configured to perform Send Descriptors Step 1050 based on a confidence level
of the image features
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,
,
,
derived in Derive Descriptors Step 1040. For example, if the confidence is
below a threshold the step
may be executed and both the image and the features sent.
[001951In an optional Compare Descriptors Step 1060, the one or more image
descriptors derived in
Derive Descriptors Step 1040 are compared with one or more image descriptors
stored locally. As
discussed elsewhere herein, these locally stored image descriptors are
associated with image classes
and/or image tags. The comparison may include calculation of a characteristic
reflecting the quality of
the match.
[00196] In some embodiments, both Send Descriptors Step 1050 and Compare
Descriptors Step 160 are
performed. In this case processing of the image descriptors can occur both on
Image Source 125A and
Image Processing System 110. Likewise, in some embodiments both Send Features
Step 1030 and
Derive Descriptors Step 1040 are performed and the image features are
processed on both
systems/devices.
[00197] In an Assign/Receive Tags Step 1070 image tags characterizing the
image are generated and/or
received. For example, if the image, image features or image descriptors have
been sent to Image
Processing System 110, then corresponding tags may be received from Image
Processing system 100 in
Assign/Receive Tags Step 1070. If a match is found between the derived
descriptors and the local stored
descriptors in Compare Descriptors Step 1060, then tags associated to the
matched locally stored image
descriptors are retrieved from local memory and assigned to the image. Tags
may be both locally
assigned and received for the same image. The Image tags are optionally
generated using image
features and/or descriptors, e.g., without Image Processing System 110
receiving the actual image.
[00198]In some embodiments, Assign/Receive Tags Step 1070 includes assigning a
classification to an
image, sending the image and the classification to Image Processing System
110, and receiving
corresponding tags back from Image Processing System 110. The tags may be
identified using the
methods illustrated in FIG. 4. The classification may be used by Image
Processing System 100 to
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,
generate the tags using Automatic Identification system 152 and/or a human
reviewer at Destination
125A.
[00199]The assigned and/or receive tags, and/or other results, are provided in
Provide Results Step 455,
as discussed elsewhere herein.
[00200] FIG. 11 illustrates a method of processing an image based on image
descriptors, according to
various embodiments of the invention. This method is typically performed on
one of Image Sources
120. In the illustrated embodiments, Steps 1010, 1020, 1040 and 1060 are
performed as described
elsewhere herein. In a Classify Image Step 1110 the image being processed is
classified based on a
match between one or more image descriptors derived in Derive Descriptors Step
1040 and image
descriptors previously stored on the one of Image Sources 120. The class or
classes assigned to the
image is the class or classes associated with the matched image descriptors
previously stored.
[00201] In a Send Step 1120 the image and the class or classes assigned to the
image are sent to Image
Processing System 110. The image is there processed as described elsewhere
herein to produce image
tags assigned to the image. The processing optionally includes use of the
class or classes to select a
human image review or to assist in automatically tagging the image.
[00202] In a Receive Tags Step 1130 the tags assigned to the image are
received by the one of Image
Sources 120 on which Receive Image Step 1010 was performed. The tags are then
presented in Provide
Results Step 455.
[00203] FIG. 12 illustrates a method of processing an image using feedback,
according to various
embodiments of the invention. This method is optionally performed on Image
Source 120A and includes
several communications between Image Source 120A and Image Processing System
110 in order to
improve tagging of an image. In a Provide Image Step 1210, an image is
provided from Image Source
120A to Image Processing System 110.
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[00204] In a Receive 1st Response Step 1220 a first response is received from
Image Processing system
110. This response may include one or more image tags. In a Provide Feedback
Step 1230 feedback
regarding the received image tags is provided from Image Source 120A to Image
Processing System 110.
This feedback is optionally manually entered by a human user of Image Source
120A and may include an
upgrade request as discussed elsewhere herein. Feedback may include correction
to one or more of the
received tags. For example, the feedback may include an indication that one of
the tags is not
representative of the image. The feedback may include a classification of the
image.
[00205]In an optional Receive 2nd Response Step 1240 a second response is
received from Image
Processing System 110. The second response is typically generated using the
feedback provided in
Provide Feedback Step 1230. In one example, considering an image of a toy car,
the first response
includes the tag "car", the feedback includes the term "toy" and the second
response includes the tags
"Fisher-Price Superwagon." The methods illustrated by FIG. 12 are optionally
used to improve the
accuracy of image tagging.
[00206] FIG. 13 and 14 illustrates methods of providing image tags based on
image descriptors,
according to various embodiments of the invention. In FIG. 13 the image
descriptors are used to
generate image tags that are then communicated over a computing network to a
source of the image
descriptors. In FIG. 14 the image descriptors are used to determine a
Destination 125 for an image. The
methods illustrated in FIGs. 13 and 14 are optionally performed in conjunction
with methods illustrated
elsewhere herein. For example the steps of these methods may be combined with
those illustrated in
FIG. 4.
[00207]Specifically, referring to FIG. 13, in a Receive Descriptors Step 1310
one or more image
descriptors characterizing an image are received at Image Processing System
110. These image
descriptors are optionally received without the associated image. Receiving
only the descriptors
typically requires less bandwidth than receiving the image. The image
descriptors are optionally
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received from Image Source 120A via Network 115 and generated using the
methods illustrated in FIGs.
or 11.
[00208] In a Compare Descriptors Step 1320 the received image descriptors are
compared to one or
more image descriptors previously stored at Image Processing System 110, e.g.,
stored in Memory 135.
This comparison is made to determine if any of the received descriptors match
the stored descriptors.
The stored descriptors are stored in association with one or more image tags
and/or classifications. For
example, one set of stored descriptors may be associated with the image tags
"oak tree."
[00209] In a Retrieve Tags Step 1330 one or more image tags are retrieved
responsive to a match
between the received descriptors and the stored descriptors. The retrieved
image tags are those
associated with matched set.
[00210] In a Provide Tags Step 1340 the retrieved image tags are provided back
to the source of the
received descriptors, e.g., to Image Source 120A. They may there be presented
to a user or otherwise
processed as described elsewhere herein.
[00211] FIG. 14 illustrates methods in which an image and data characterizing
the image are processed
at Image Processing Server 110. In a Receive Image & Data Step 1410 the image
and the data
characterizing the image are received at Image Processing Server 110. The data
characterizing the
image can include, for example, a classification of the image or image
descriptors characterizing the
image. The image and characterizing data are optionally received from Image
Source 125A. Receive
Image & Data Step 1410 is optionally an embodiment of Receive Image Step 410
and Receive Source
Data Step 420.
[00212] In a Determine Destination Step 1420 a destination for the image is
determined based on the
data characterizing the image. The destination may be one of Destination 125
and/or Automatic
Identification System 152. For example, if the data characterizing the image
includes a specific
classification and the determined destination may be one of Destination 125
being associated with a
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human image review having expertise in that classification. Determine
Destination Step 1420 is
optionally an embodiment of Determine Destination Step 465.
[00213] In a Post Image Step 1430 the image, and optionally the
classification, are communicated to the
determined destination. In a Receive Tags Step 1440 one or more image tags are
received. The image
tags being based on the image and being selected to characterize the image. In
a Provide Tags Step
1340 the image tags are provided to the source of the image, e.g. Image Source
125A. Post Image Step
1430 is optionally an embodiment of Post Image Step 470.
[00214] FIG. 15 illustrates methods of prioritizing image tagging, according
to various embodiments of
the invention. In these methods Image Ranker 190 is used to assign a priority
to an image and the
priority is used to determine how, if at all, the image is tagged. In Receive
Image Step 410 an image is
received at Image Processing System 110 as discussed elsewhere herein. The
image may be from one of
Image Sources 120, and may be received by crawling webpages for images. In
some embodiments one
or more of Image Source 120 include logic configured to crawl websites and
retrieve images from these
websites. Information received along with the image may include data regarding
a webpage from which
the image was retrieved. For example, the image may be received along with
text and metadata from
the webpage, data indicating how often the webpage is loaded (viewed), a URL
of the webpage, and/or
any other data on which image priority may be determined as discussed
elsewhere herein.
[00215] In an Assign Priority Step 1520, Image Ranker 190 is used to
automatically assign a priority to
the received image. The priority is optionally represented by a numerical
value from 1-100, by a letter
grade, or the like. Priority optionally implies an (ordered) ranking of
images. As described elsewhere
herein, the priority may be determined based on a wide variety of factors.
[00216] In a Determine Processing Step 1530 a method of tagging (processing)
the image is determined.
The determination is based on the assigned priority of the image. In some
embodiments, images with
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lowest priority are not processed (tagged) at all. The methods of tagging
include automated tagging
and/or manual tagging by a human reviewer, as described elsewhere herein.
100217] In an optional Automatic Tagging Step 1540 the image is tagged using
Automatic Identification
System 152. Automatic Tagging Step 1540 is optional in embodiments where the
method of tagging
determined in Determine Processing Step 1530 does not include use of Automatic
Identification System
152. Automatic Tagging Step 1540 is optionally performed prior to Assign
Priority Step 1520. For
example, an image may be tagged using automatic Identification System 152, and
a confidence level for
the automatically generated tags may then be used in Assign Priority Step 1520
to determine a priority
for manual (human) tagging. If the confidence of the automatically generated
tags is high then the
priority for manual tagging may be set low, and if the confidence is
relatively low then the priority for
manual tagging may be set relatively high.
[00218]In an optional Manual Tagging Step 1550 the image is sent to one of
Destinations 125 for
tagging by a human reviewer. The image may be sent with tags generated using
Automatic
Identification System 152 and/or a variety of other information as described
elsewhere herein. Manual
Tagging Step 1550 may include any of the steps illustrated by FIGs. 6-8.
[00219]In an optional Auction Tag Step 1560 an advertisement is assigned to
the image for display on a
webpage. This webpage is optionally the webpage from which the image was
obtained in Receive Image
Step 410. Auction Tag Step 1560 is optionally performed in real-time as a
request for the webpage is
received. At that time, the tag(s) assigned to the image can be auctioned off
to the party willing to
provide the greatest consideration for placing an advertisement over or beside
the image. Auction Tag
Step 1560 is optionally performed using Advertising System 180 and the auction
process may be
managed by a third party, such as Google's Adsence .
[00220]In an optional Retag Step 1570, an image is retagged. Retag Step 1570
may include an analysis
of how often advertisement(s) assigned to the image are clicked as compared to
an expected click rate.
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For example if advertisements assigned to an image based on a first tagging
are not clicked on at an
expected rate, then the tags may not be an optimal representation of the
image. The image may be
retagged in an attempt to improve the click rate of assigned advertisements.
Retag Step 1570 may
include any of the tagging methods disclosed herein, e.g., those methods
discussed in relation to FIGs. 6-
8 and 15. Retag Step 1570 may use the knowledge that the tags resulting from
the first tagging were
not optimal.
100221]The methods illustrated by FIG. 15 may also be applied to image
sequences, e.g., video. The
image sequence may be presented in a browser or using a variety of alternative
applications. For
example, video may be provided to a member of Image Sources 120 from a website
such as
youtube.com or from streaming services such as Netflix, Comcast cable
television, direct TV, Ruku or
Hulu. Factors used to determine if an image within a video should be manually
tagged include: how
often the video is viewed and/or the estimated value of expected tags.
Expected tags may be indicated
by an automatic review of the image using Automated Identification System 152,
dialog within the
video, text accompanying the video (e.g., a description, caption or title),
and/or the like. The
advertisement may be a video appended at the beginning or end of the image
sequence, or spliced
within the image sequence. Thus, the advertisement and video can be presented
together in
association. The advertisement may include an overlay placed over part of the
image sequence,
typically a part including the tagged image.
[00222]Several embodiments are specifically illustrated and/or described
herein. However, it will be
appreciated that modifications and variations are covered by the above
teachings and within the scope
of the appended claims without departing from the spirit and intended scope
thereof. For example, the
images discussed herein are optionally part of a video sequence of a video.
Human image reviews may
provide image tags at Destinations 125 using audio input. The audio input can
be converted to text in
real-time using audio to text conversion logic disposed on Destinations 125
and/or Image Processing
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,
,
System 110. Image tags are optionally processed by spellcheck logic. As used
herein, the term "Real-
time" means without unnecessary delay such that a user can easily wait for
completion. The systems
and methods described herein are optionally used to tag audio content, such as
music or dialog. This
audio content may be part of a video or otherwise associated with an image. In
some embodiments,
audio content is automatically converted to text and this text is used to
assist in manually or
automatically tag an image. Text generated from audio content may be used in
manners similar to
those described herein for text found on a webpage including an image, to
assist in tagging the image.
[00223]The embodiments discussed herein are illustrative of the present
invention. As these
embodiments of the present invention are described with reference to
illustrations, various
modifications or adaptations of the methods and or specific structures
described may become apparent
to those skilled in the art. All such modifications, adaptations, or
variations that rely upon the teachings
of the present invention, and through which these teachings have advanced the
art, are considered to
be within the spirit and scope of the present invention. Hence, these
descriptions and drawings should
not be considered in a limiting sense, as it is understood that the present
invention is in no way limited
to only the embodiments illustrated.
[00224]Computing systems referred to herein, (e.g., Image Processing System
110, Images Sources 120
and Destinations 125), can comprise an integrated circuit, a microprocessor, a
personal computer, a
server, a distributed computing system, a communication device, a network
device, or the like, and
various combinations of the same. A computing system may also comprise
volatile and/or non-volatile
memory such as random access memory (RAM), dynamic random access memory
(DRAM), static
random access memory (SRAM), magnetic media, optical media, nano-media, a hard
drive, a compact
disk, a digital versatile disc (DVD), and/or other devices configured for
storing analog or digital
information, such as in a database. The various examples of logic noted above
can comprise hardware,
firmware, or software stored on a computer-readable medium, or combinations
thereof. A computer-
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readable medium, as used herein, expressly excludes paper. Computer-
implemented steps of the
methods noted herein can comprise a set of instructions stored on a computer -
readable medium that
when executed cause the computing system to perform the steps. A computing
system programmed to
perform particular functions pursuant to instructions from program software is
a special purpose
computing system for performing those particular functions. Data that is
manipulated by a special
purpose computing system while performing those particular functions is at
least electronically saved in
buffers of the computing system, physically changing the special purpose
computing system from one
state to the next with each change to the stored data. The logic discussed
herein may include hardware,
firmware and/or software stored on a computer readable medium. This logic may
be implemented in
an electronic device to produce a special purpose computing system.
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