WO2006122164A2 - Systeme et procede permettant l'utilisation d'images capturees par reconnaissance - Google Patents

Systeme et procede permettant l'utilisation d'images capturees par reconnaissance Download PDF

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Publication number
WO2006122164A2
WO2006122164A2 PCT/US2006/018016 US2006018016W WO2006122164A2 WO 2006122164 A2 WO2006122164 A2 WO 2006122164A2 US 2006018016 W US2006018016 W US 2006018016W WO 2006122164 A2 WO2006122164 A2 WO 2006122164A2
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WIPO (PCT)
Prior art keywords
image
person
recognition
images
information
Prior art date
Application number
PCT/US2006/018016
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English (en)
Other versions
WO2006122164A3 (fr
Inventor
Salih Burak Gokturk
Munjal Shah
Azhar Khan
Dragomir Anguelov
Vincent Vanhoucke
Kuang-Chih Lee
Diem Vu
Daniel Yang
Original Assignee
Riya, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US11/246,742 external-priority patent/US7519200B2/en
Priority claimed from US11/246,589 external-priority patent/US7809192B2/en
Priority claimed from US11/246,434 external-priority patent/US7783135B2/en
Priority claimed from US11/246,741 external-priority patent/US7809722B2/en
Application filed by Riya, Inc. filed Critical Riya, Inc.
Priority to EP06770158A priority Critical patent/EP1889207A4/fr
Publication of WO2006122164A2 publication Critical patent/WO2006122164A2/fr
Publication of WO2006122164A3 publication Critical patent/WO2006122164A3/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • G06V20/63—Scene text, e.g. street names
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/179—Human faces, e.g. facial parts, sketches or expressions metadata assisted face recognition

Definitions

  • the disclosed embodiments relate generally to the field of digital image processing. More particularly, the disclosed embodiments relate to a system and method for enabling the use of captured images.
  • FIG. 1 illustrates a sequence of processes which may be performed independently in order to enable various kinds of usages of images, according to an embodiment.
  • FIG. 2 illustrates an embodiment in which the correlation information may be used to create objectified image renderings, as well as enable other functionality
  • FIG. 3 describes a technique for detecting a face in an image, under an embodiment of the invention.
  • FIG. 4 illustrates a technique for recognizing a face in an image, under an embodiment of the invention.
  • FIG. 5 illustrates a technique for recognizing a person in an image using clothing and/or apparel worn by the person in the image, under an embodiment of the invention.
  • FIG. 6 is a block diagram illustrating techniques for using recognition information from different physical characteristics of persons in order to determine a recognition signature for that person, under an embodiment of the invention.
  • FIG. 7 illustrates a method for correlating an identity of a person with recognition information for that person, under an embodiment of the invention.
  • FIG. 8 illustrates an embodiment in which clustering of images is performed programmatically.
  • FIG. 9 illustrates a basic method is described for recognizing and using text when text is provided on objects of an image, under an embodiment of the invention.
  • FIG. 1OA provide individual examples of features, provided as block patters, provided for purpose of detecting the presence of text in an image, under an embodiment of the invention.
  • FIG. 1OB and FIG. 1OC illustrate examples of a text stretching post-processing technique for text in images, under an embodiment of the invention.
  • FIG. 1 OD illustrates examples of a text tilting post-processing technique for text in images, under an embodiment of the invention.
  • FIG. 11 illustrates a technique in which a detected and recognized word in one image is then spanned across a set of images for purpose of tagging images in the set with the recognized text, under an embodiment of the invention.
  • FIG. 12 illustrates a system on which one or more embodiments of the invention may be performed or otherwise provided.
  • FIG. 13 illustrates person analysis component for use in embodiments such as described in FIG. 12 with greater detail, under an embodiment of invention.
  • FIG. 14A is a graphical representation of the Markov random field, which captures appearance and co-appearance statistics of different people, under an embodiment of the invention.
  • FIG. 14B is another graphical representation of the Markov random field, incorporating clothing recognition, under an embodiment of the invention.
  • FIG. 15 illustrates a system for text recognition of text carried in images, under an embodiment of the invention.
  • FIG. 16 illustrates a system in which searching for images based on their contents can be performed, under an embodiment of the invention.
  • FIG. 17 describes a method for creating objectified image renderings, under an embodiment of the invention.
  • FIG. 18 is a representation of an objectified image file as rendered, under an embodiment of the invention.
  • FIG. 19 is a representation of an objectified image file as rendered, under another embodiment of the invention.
  • FIG. 20 provides an example of an objectified image rendering, where metadata is displayed in correspondence with recognized objects in the image, under an embodiment of the invention.
  • FIG. 21 illustrates a basic system for enabling similarity matching of people, under an embodiment of the invention.
  • FIG. 22 illustrates an embodiment in which an image is selected for a text content.
  • Embodiments described herein provide for various techniques that enable the programmatic of digitally captured images using, among other advancements, image recognition.
  • Embodiments described herein mine image files for data and information that enables, among other features, the indexing of the contents of images based on analysis of the images. Additionally, images may be made searchable based on recognition information of objects contained in the images.
  • Other embodiments provide for rendering of image files in a manner that makes recognition information about objects those images usable. Numerous other applications and embodiments are provided.
  • embodiments of the invention enable users to (i) categorize, sort, and label their images quickly and efficiently through recognition of the contents of the images, (ii) index images using recognition, and (iii) search and retrieve images through text or image input.
  • recognition may be performed on persons, on text carried on objects, or on other objects that are identifiable for images.
  • Techniques are also described in which images may be rendered in a form where individual objects previously recognized are made selectable or otherwise interactable to the user.
  • Network services are also described that enable online management and use of consumer photographs.
  • embodiments contemplate amusement applications where image recognition may be used to match people who are look- alikes.
  • Social network and image-based as insertion applications are also contemplated and described with embodiments of the invention.
  • An embodiment provides for enabling retrieval of a collection of captured images that form at least a portion of a library of images. For each image in the collection, a captured image may be analyzed to recognize information from image data contained in the captured image. An index may be generated based on the recognized information. Using the index, functionality such as search and retrieval is enabled. Various recognition techniques, including those that use the face, clothing, apparel, and combinations of characteristics may be utilized. Recognition may be performed on, among other things, persons and text carried on objects.
  • embodiments enable the search and retrieval of images based on recognition of objects appearing in the images being searched. Furthermore, one or more embodiments contemplate inputs that correspond to text or image
  • an input may correspond to an image specified by a user, and that image is used to generate the search criteria from which other images are found.
  • embodiments provide for detection and recognition of faces. Additionally, one or more embodiments described enable recognition of persons to be based at least in part on clothing or apparel worn by those persons.
  • a person may be detected from a captured image. Once the detection occurs, recognition information may be generated from the clothing or apparel of the person.
  • the person is detected first, using one or more markers indicating people (e.g. skin and/or facial features), and then the position of the clothing is identified from the location of the person's face.
  • the recognition information of the clothing may correlate to the coloring present in a region predetermined in relative location to the detected face, taking into account the proportionality provided from the image.
  • information about captured images be determined by identifying a cluster of images from a collection of captured images.
  • the cluster may be based on a common characteristic of either the image or of the image file (such as metadata).
  • a recognition signature may be determined for a given person appearing in one of the cluster of images.
  • the recognition signature may be used in identifying a recognition signature of one or more persons appearing in any one of the cluster of images.
  • the persons in the other images are all the same person, thus recognition of one person leads to all persons (assuming only one person appears in the images in the cluster) in the cluster being identified as being the same person.
  • a collection of images may be organized using recognition.
  • an embodiment provides for detecting and recognizing texts carried on objects.
  • information related to the text may be used to categorize the image with other images.
  • the text may indicate a location because the name of the city, or of a business establishment for which the city is known, appears on a sign or other object in the image.
  • recognition is performed on captured images for purpose of identifying people appearing in the images.
  • image data from the captured image is analyzed to detect a face of a person in the image.
  • the image data is then normalized for one or more of the following: lighting, orientation, and size or relative size of the image.
  • recognition may also be performed using more than one marker or physical characteristic of a person.
  • a combination of two or more markers are used.
  • embodiments contemplate generating a recognition signature based on recognition information from two or more of the following characteristics: facial features (e.g. eye or eye region including eye brow, nose, mouth, lips and ears), clothing and/or apparel, hair (including color, length and style) and gender.
  • facial features e.g. eye or eye region including eye brow, nose, mouth, lips and ears
  • clothing and/or apparel e.g. eye or eye region including eye brow, nose, mouth, lips and ears
  • hair including color, length and style
  • metadata about the image file such as the time the image was captured, or the location from which the image was captured, may be used in combination with recognition information from one or more of the features listed above.
  • content analysis and data inference is used to determine a recognition signature for a person. For example, relationships between people in images may be utilized to use probabilities to enhance recognition performance.
  • images are displayed to a user in a manner where recognized objects from that image are made user-interactive.
  • stored data that corresponds to an image is supplemented with metadata that identifies one or more objects in the captured image that have been previously recognized.
  • the captured image is then rendered, or made renderable, using the stored data and the metadata so that each of the recognized objects are made selectable.
  • a programmatic action may be performed, such as the display of the supplemental information, or a search for other images containing the selected object.
  • an image viewing system comprising a memory that stores an image file and metadata that identifies one or more objects in the image file.
  • the one or more objects have recognition information associated with them.
  • a user-interface or viewer may be provided that is configured to use the metadata to display an indication or information about the one or more objects.
  • image data is intended to mean data that corresponds to or is based on discrete portions of a captured image.
  • the image data may correspond to data or information about pixels that form the image, or data or information determined from pixels of the image.
  • the terms "recognize”, or “recognition”, or variants thereof, in the context of an image or image data is meant to means that a determination is made as to what the image correlates to, represents, identifies, means, and/or a context provided by the image. Recognition does not mean a determination of identity by name,
  • programmatic means through execution of code, programming or other logic.
  • a programmatic action may be performed with software, firmware or hardware, and generally without user-intervention, albeit not necessarily automatically, as the action may be manually triggered.
  • programmatic elements often referred to as modules or components, although other names may be used.
  • Such programmatic elements may include a program, a subroutine, a portion of a program, or a software component or a hardware component capable of performing one or more stated tasks or functions.
  • a module or component can exist on a hardware component independently of other modules/components or a module/component can be a shared element or process of other modules/components, programs or machines.
  • a module or component may reside on one machine, such as on a client or on a server, or a module/component may be distributed amongst multiple machines, such as on multiple clients or server machines.
  • Any system described may be implemented in whole or in part on a server, or as part of a network service.
  • a system such as described herein may be implemented on a local computer or terminal, in whole or in part. In either case, implementation of system provided for in this application may require use of memory, processors and network resources (including data ports, and signal lines (optical, electrical etc.), unless stated otherwise.
  • Embodiments described herein generally require the use of computers, including processing and memory resources.
  • systems described herein may be implemented on a server or network service.
  • Such servers may connect and be used by users over networks such as the Internet, or by a combination of networks, such as cellular networks and the Internet.
  • networks such as the Internet
  • one or more embodiments described herein may be implemented locally, in whole or in part, on computing machines such as desktops, cellular phones, personal digital assistances or laptop computers.
  • memory, processing and network resources may all be used in connection with the establishment, use or performance of any embodiment described herein (including with the performance of any method or with the implementation of any system).
  • Machines shown in figures below provide examples of processing resources and computer-readable mediums on which instructions for implementing embodiments of the invention can be carried and/or executed.
  • the numerous machines shown with embodiments of the invention include processor(s) and various forms of memory for holding data and instructions.
  • Examples of computer-readable mediums include permanent memory storage devices, such as hard drives on personal computers or servers.
  • Other examples of computer storage mediums include portable storage units, such as CD or DVD units, flash memory (such as carried on many cell phones and personal digital assistants (PDAs)), and magnetic memory.
  • Computers, terminals, network enabled devices are all examples of machines and devices that utilize processors, memory, and instructions stored on computer- readable mediums. [050] OVERVIEW
  • FIG. 1 illustrates a sequence of processes which may be performed independently or otherwise, in order to enable various kinds of usages of images, according to an embodiment.
  • a sequence such as illustrated by FIG. 1 is intended to illustrate just one implementation for enabling the use of captured images.
  • each of the processes in the sequence of FIG. 1 may be performed independently, and with or without other processes described.
  • other processes or functionality described elsewhere in this application may be implemented in addition to any of the processes illustrated by FIG. 1.
  • FIG. 1 illustrates an embodiment that utilizes a sequence of processes, each of the processes and sub-processes that comprise the described sequence may in and of itself form an embodiment of the invention.
  • image data 10 is retrieved from a source.
  • the image data 10 may correspond to a captured image, or portion or segment thereof.
  • a system may be implemented in which one or more types of objects may be detected and recognized from the captured image.
  • One or more object detection processes 20 may perform detection processes for different types of objects identified from the image data.
  • the object detected is a person, or a portion of a person, such as a face, a body, a hair or other characteristic.
  • Numerous other types of objects may be detected by the one or more object detection processes, including (i) objects carrying text or other alphanumeric characters, and (ii) objects associated with people for purpose of identifying an individual.
  • An example of the latter type of object includes apparel, such as a purse, a briefcase, or a hat.
  • 5 /8/2006 objects that can be detected from object detection processes include animals (such as dogs or cats), and landmarks.
  • Detected objects 22 are then analyzed and possibly recognized by one or more object recognition processes 30. Different recognition results may be generated for different types of objects.
  • the recognition processes 30 may identify or indicate (such as by guess) one or more of the following for a given person: identity, ethnic classification, hair color or shape, gender, or type (e.g. size of the person).
  • the recognition information may correspond to alphanumeric characters. These characters may be identified as guesses or candidates of the actual text carried on the detected object.
  • the recognition information may indicate or identify any one or more of the following: what the detected object is, a class of the detected object, a distinguishing characteristic of the detected object, or an identity of the detected object.
  • recognition information may recognize to different levels of granularity.
  • the recognition information may correspond to a recognition signature that serves as a relatively unique identifier of that person.
  • a recognition signature may be used to identify an individual from any other individual in a collection of photographs depicting hundreds, thousands, or even millions of individual (depending on the quality and/or confidence of the recognition).
  • recognition information may only be able to identify a person as belonging to a set of persons that are identifiable from other persons in the same pool of people.
  • the recognition information may identify people by ethnic class or gender, or identify a person as being one of a limited number of matching possibilities.
  • recognition information is a quantitative expression.
  • a recognition signature may correspond to a highly dimensional vector or other dimensional numerical value.
  • a correlation process 40 can be used to correlate the detected and recognized object of the image with data and information items, and/or other information resources.
  • Various types of functionality may be enabled with the correlation process 40, including for example, search, categorization, and text object research.
  • the recognized object is a person, or a portion of a person.
  • the correlation process 40 generates correlation information 42 that is an identity, or more generally identification information to the person.
  • correlation information 42 is an identity, or more generally identification information to the person.
  • the recognized object carries text, and the correlation information 42 assigns meaning or context to the text.
  • correlation process 40 may, for a recognized face, generate correlation information 42 that correlates the recognition information 32 with other images that have been determined to carry the same recognized face.
  • one recognition signature may be correlated to a collection of digital photographs carrying the same person.
  • Examples of the types of information items and resources that recognized objects can be correlated to include some or all of the following: other images with the same recognition information or signature, clothing recognition information, text based content associated with a recognized object, audio or video content associated with the recognized object, other images that contain objects with similar but not the same detected object, or third-party Internet search engines that can retrieve information in response to specified criteria.
  • the correlation process 40 may correlate recognition information 32 in the form of a string of alphanumeric characters, to a meaning or context, such as to a proper name, classification, brand-name, or dictionary meaning.
  • the correlation process 40 may generate correlation information 42 that indirectly correlates recognition information 32 to recognized word.
  • the recognition information 32 may correlate the popular name of a hotel with a city where the hotel is located.
  • correlation information 42 resulting from the correlation process 40 may be stored or otherwise used for various purposes and functionality.
  • correlation information 42 may be provided in the form of metadata that is carried with an image file, or it may be in the form of index data that forms a portion of an index.
  • index data may be in the form of index data that forms a portion of an index.
  • one embodiment provides for an index that associates recognition information of a detected object with images that contain the same recognized object.
  • FIG. 2 illustrates an embodiment in which the correlation information 42 may be used to create objectified image renderings 50, as well as enable other functionality.
  • the objectified image renderings are images that are displayed with individually detected objects being separately selectable, as a form of a graphic user-interface feature. As described with FIG. 18, for example, the objectified image rendering 50 enables detected/recognized objects to be made in focus and/or selectable by input operations of the user provided in selectable
  • a user may hover a pointer over a face in the image and have that image be made selectable.
  • the user may enter an input 52 that causes a programmatic function to be performed in which the correlation information 42 is used to present additional information from the object selected from the rendering 50.
  • Further description of objectified image renderings 50 are provided elsewhere in this application.
  • the objectified image renderings 50 may (but not necessarily) be provided as a precursor to other functionality that takes use of the object detection process 20, object recognition process 30, and object correlation process 40.
  • a search feature 60 may be enabled that enables a user to specify a selectable object from a rendering as a search input.
  • a user can specify an image as the search input. For example, if the objectified image rendering 50 displays a party scene with a recognized face provided as a selectable feature, a user can manipulate a mouse or other pointer device to select the face as input. The face then becomes the search criteria, and a search operation may be performed using the selected face. As will be described, the search may be performed on a library of images residing locally or over a network (in whole or in part).
  • categorization or sort feature 66 where images are clustered are grouped together based on a common feature (e.g. a recognized object).
  • a common feature e.g. a recognized object
  • the user's input may correspond to a selection of a selectable object in an image (such as described with FIG. 18).
  • selection of the face may result in other images with the same face being clustered together.
  • An extrapolation feature 70 is another type of functionality that can be provided in connection with the objectified image renderings 50.
  • the extrapolation feature may take a recognized object (made selectable in the objectified image renderings 50) and make that selection the basis of an intelligent information or content gathering (including other images). For example, if the recognized object corresponds to recognized text carried on an object, a context of that text, as well as other useful information about the text (or the object carrying it) may be provided. With a face, an embodiment may provided that the extrapolation feature 70 presents similar faces (people who look like the recognized face), as well as celebrities or dogs who look like the recognized face.
  • Embodiments described herein provide two types of objects as being of particular interest for detection and recognition: (i) persons, and (ii) objects carrying text. However, other types of objects may also be of interest to one or more embodiments, including dogs, cats, geographic sites and landmarks, much of the details provided in embodiments described below are specific to persons and text-carrying objects. [065] PERSONS
  • recognition information for a person may yield the identity of the person when recognition can be well- performed. However, recognition information can also be performed to a lesser degree that identity determination, such as when the picture being used is of poor quality, or when the specific recognition algorithm is not capable of yielding the identity.
  • the result of the recognition algorithm may be a class (gender or race) of people that the person belongs to, or a set of people that are candidates as being the person in the image.
  • the result of the recognition algorithm may be similar looking people, or even similar things (such as animals).
  • recognition of persons involves (i) detection of a person in an image being analyzed, and (ii) recognition of the detected person.
  • Detection and recognition may employ specific characteristics, features, or other recognizable aspects of people in pictures.
  • each of detection and/or recognition may employ facial features, clothing, apparel, and other physical characteristics in determining recognition information about a person.
  • metadata from the captured image such as the date and time when the image was captured, may be used to facilitate recognition. If metadata exists about the location of where the image was taken (e.g. such as through a base station stamp if the picture is taken from a cellular telephone device, or from global- positioning information integrated into the device), the location information may also be used to aid recognition.
  • one or more embodiments may employ a context, setting, or information about other objects (such as recognition information about other persons appearing in an image) to aid the recognition of a given person in an image.
  • detection of a person is a separately performed process from recognition of the person.
  • the detection of persons may be accomplished in-part by analyzing, scanning, or inspecting images for a feature common to at least most individuals.
  • a feature that signals the presence of a particular object or type of object may be referred to
  • marker feature As a marker feature.
  • One or more embodiments provide for the use of the human face as the primary physical feature from which detection and recognition of a person in an image is performed.
  • a specific type of marker feature is a facial feature, such as eyes (eye brow, eye socket, iris or eyelid), nose (nose tip, nostril) or mouth (lips, shape).
  • a specific type of feature contemplated is a facial feature.
  • other examples of marker features include clothing, apparel, hair style, shape or color, and body shape.
  • detection may be performed as a precursor to face recognition, followed by identity determination and/or classification determination, including ethnic and gender determination. Marker features may form the start of detection and/or validate the detection.
  • an embodiment such as provided by FIG. 3 provides for a learning based face detection algorithm.
  • Li step 210 a training phase is applied where a training set of face and non-face images are collected, and a classification algorithm, such as Support Vector Machines, Neural Networks, or Hidden Markov Models, Adaboost classifiers are trained.
  • the training faces used may accommodate various types of faces or facial markers, including eyes (eyebrows and socket), nose or mouth.
  • step 220 the input image is traversed through discrete image elements across at least a relevant portion of the image. When implemented on digital images, this step may be performed by pixel-by-pixel traversal across an image file. At each pixel, a variable size window around the pixel is tested to be face or non-face using the learnt classification algorithm from step 210.
  • a step 230 provides that a detected face is then tested again using a color model to eliminate false positives.
  • the main idea is to reject any face that does not have the same color as skin color.
  • a skin color model may be implemented in the form of a look up table.
  • the lookup table may include data indicating the probability that a particular color (or pixel) is skin.
  • Different methods exist to construct a skin color model In one implementation, a histogram of the hue channel may be used on a large sample of skin images. In other implementation, YcrCb or red-green-blue (RGB) color spaces can be used.
  • a new detection confidence may be computed by taking the weighted average (that give more weight for the center part of the face) of all pixels in the detected face region. The final confidence is then the combination between this
  • step 240 provides that the face detection may be validated using marker detection.
  • eye detection may be used. Eye detection may be performed within a region of the image corresponding to where the unverified face image is detected as being. This further eliminates false positives.
  • the relative location of eyes with respect to one another, or the absolute location of individual eyes within the face image, or the confidence of the eye detection may be used to confirm that a face has been detected.
  • Marker detection itself may be performed using a training algorithm. For example, a training set of eye images may be used, in connection with a classification algorithm (e.g. Support Vector Machine, AdabOost), to train an algorithm to detect the presence of eyes.
  • a classification algorithm e.g. Support Vector Machine, AdabOost
  • step 320 the detected face is normalized.
  • normalization involves one or more of the following: (i) scaling each detected face, (ii) providing the detected face with a normalized pose, and (iii) normalizing the effects of lighting.
  • the scale is normalized into a fixed window size so that different-sized windows of faces can be compared to each other.
  • Pose normalization may be addressed in part by determining the eye locations (or other facial feature).
  • the located eye may correspond to a determination of the eye socket, eyebrow or other part of the eye region.
  • the in-plane rotations are corrected if there is an angle between the eye locations.
  • a detection method similar to the face detection can be used to detect the eyes.
  • Normalization of the lighting conditions on the face may be normalized using any one of a lighting normalization technique.
  • the lighting normalization technique employed utilizes histogram equalization. Histogram equalization translates the distribution of a histogram of a given image to a uniform distribution in order to increase the dynamic range of the given image.
  • Linear ramp also sometimes known as the "facet” model, is another traditional approach that fits a linear intensity "ramp” to the image by minimizing the error
  • facet also sometimes known as the "facet” model
  • the cropped face image based on the eye location may still contains slight rotation and scale variation. Therefore, the next registration process tries to align the face features to reduce the variation by a generic face model or other component face features, such as nose tip and corners, and lip center and corners.
  • the component face feature classifiers can be trained by standard Adaboost or Support Vector Machine algorithm.
  • More than one normalization process or sequence may be used to produce a better normalized image.
  • a belief propagation inference can further help to find the miss-detected face component features, as well as adjust the location of the face component features.
  • Other implementations may provide for the use of histogram and Gabor filter response to detect component face features (e.g. such as eye brow, eye socket, nose, lips).
  • the better normalized face image is obtained by iteratively fitting a generic face template with the perturbation of the eye locations.
  • an advanced technique of normalization includes face feature alignment and pose correction.
  • a component face feature alignment tries to find a two dimensional (affine) transformation by least-square fitting to align the facial feature points with the same feature points on the generic face template.
  • the pose correction consists of two steps. The first is a pose estimation problem, where one goal is to identify the best pose to which the input face image belongs with the highest appearance similarity. The second step is to update the appearance of each face component. The result from the first step is applied to find a set of pre-training images that are expected to appear similar to the specific face component in frontal pose. Then the specific face component is updated by these pre-training face component images to minimize the reconstruction error.
  • a lighting normalization can be applied across different people in an image or set of images from an event. First, all the faces are collected from each image. Then, a lighting normalization technique, such as
  • V8/20Q6 PfCT/ LISOEk ⁇ Jt SOiIB histogram equalization is applied on the collection of faces. This way, the skin color information is retained across different people.
  • step 330 provides that a recognition signature is determined for each face.
  • One embodiment provides for use of Principal Component Analysis (PCA) 5 or a similar analysis technique, to determine the recognition signature.
  • PCA Principal Component Analysis
  • a large training set of faces is obtained.
  • the training set of faces may include faces or facial features from people of different races, gender, or hair color.
  • a training set of facial images may incorporate a characteristic for a nose, eye region, mouth or other facial feature.
  • a PCA technique may be applied on this set of training faces, and singular vectors are obtained. Any face in the testing set is represented by their projection onto the singular vector space. This results in a recognition signature (v;) of a particular face.
  • step 340 once the recognition signatures (features) are obtained for each face, the faces need to be matched to identities.
  • the matching of recognition signatures to identities is an example of a correlation process. Numerous techniques may be employed to perform this step. These techniques include programmatic, manual or combination techniques. Different correlation techniques are described elsewhere in this application.
  • LDA linear discriminant analysis
  • PCA multi-linear analysis
  • Other embodiments may employ multi-linear analysis (Tensor Face), or alternatively inter and intra face subspace analysis.
  • the results of hair, gender, and ethnicity classification, as well as the clothing information can be also applied as cascade classifiers to improve the face recognition performance.
  • Support Vector Machine SVM
  • Hair detector can be learned by first picking up the histogram of the hair at certain areas above the face, and then the whole hair areas can be detected by iteratively growing the hair region with the similar hair color.
  • the step of detecting a person or face may be performed as an additional step of recognition. If steps 310-330 are performed and the result of the recognition is a bad signature or recognition (e.g. a signature that does not map to a typical recognition value for a person or face), then the result returned as a result of the recognition may be that no face was detected. Thus, the process of detection may actually be a result of the recognition process. Further teachings on detecting text carried on objects in images, and
  • facial recognition can provide recognition with a high level of granularity (e.g. uniquely define or identify the person)
  • other physical characteristics of persons can be used to generate recognition information, particularly when other features are combined with facial feature recognition, and/or when the library of images is relatively small.
  • One type of physical feature of persons that can provide useful recognition information is clothing and/or apparel. Clothing may include the shirt, jacket, sweater, pullover, vest, socks, or any other such item. Apparel may include a hat, eyewear (such as prescription or sun glasses), scarf, purse, backpack, jewelry (including watches) or any other such item worn or carried by a person.
  • FIG. 5 illustrates a technique for recognizing a person in an image using clothing and/or apparel worn by the person in the image, under an embodiment of the invention.
  • a face of a person is detected.
  • the detection other person may utilize a facial feature, such as the nose, eye area or mouth.
  • a method such as shown by FIG. 3 is a precursor to performing a method such as described by FIG. 4 and FIG. 5.
  • image data is extracted from a window located a distance from the detected face.
  • the region from which the image data is extracted may indicate the type of clothing or apparel that may be identified from that window.
  • the window may be generated below the detected face, so that the image data will indicate whether the person is wearing a shirt, jacket or sweater.
  • the window may be provided above the face, to indicate what kind (if any at all) of hat a person is wearing. Proportionality, with respect to the size of the detected face in the image, may enable the window to be drawn at regions of the person that indicate waistline or leg area, so that the resulting extracted image data indicates, for example, belts, pants or shorts worn by the person.
  • step 430 once the region is identified, image data from the window is quantified, under an initial assumption that the image data corresponds to clothing.
  • a clothing vector (ci) is extracted from this window.
  • a color histogram of the clothing region is obtained. Different color spaces can be used for this instance, such as RGB color space, or YUV color space can be used.
  • the histogram bins can be obtained using various methods. For example, a vector quantization algorithm can be used, and a K-Meansalgorithm can be used to choose histogram centers. In another embodiment, uniform histogram centers can be used.
  • the histogram is obtained by counting the color values in the clothing region towards the histogram bins. In one embodiment, each color value gives a single vote to the closest histogram bin center.
  • each color value distributes a single vote to all histogram bins proportional to the inverse distance of the bin centers.
  • a K-Means or an adaptive K-Means algorithm may be applied on the clothing image.
  • the K-Means algorithm may need a static input for K, corresponding to, for example, the number of colors expected in the portion of an image containing color.
  • the adaptive K-means algorithm starts with a higher K limit and determines from that limit how many colors are in the image.
  • This K color centers may be stored as a representation vector or quantity for clothing.
  • an Earth-Mover's distance can be used to match two color features, while comparing the clothing of two individuals.
  • a given color (such as red) may be quantified in terms of how much it occupies in a given window of an image. An assumption may be made that distortion of colors exist, so if there is a matching in quantity of a color in a given window, it is possible for a match to be determined, pending outcome of other algorithms.
  • recognition information While generating recognition information from clothing and apparel may not seem to be indicative of the identity of a person, such recognition information when combined with other data can be particularly revealing.
  • a recognition algorithm may be performed that assumes an individuals clothing will not change, in the course of a set time range, such as over the course of a day, or a portion of the day. Accordingly, if the identity of a detected person is known in one image taken at a given time, any subsequent image taken in a duration from that given time having (i) a detected face, and (ii) clothing matching what the known person was wearing in the image taken at the given time. Clothing information can be advantageous because it is less computationally intensive, and requires less picture detail, as compared to face recognition.
  • one or more embodiments of the invention contemplate the use of multiple recognition sources in determining recognition signatures or information about persons.
  • clothing/apparel and facial recognition may be combined to determine identity of detected persons in a collection of images.
  • the technique of combining multiple sources of information is sometimes called "Double Binding".
  • the input to the identity recognition algorithm is digitally captured images, such as photographs captured by consumer-level users.
  • An embodiment contemplates a service that collects images from multiple users over a network such as the Internet, although other implementations may be provided for just a single user running a local program.
  • photographs can be grouped using different metrics, such as the images being part of the same directory, or having a similar timestamp.
  • the web photographs can be grouped by the timestamps of the photographs, or the specific web page (URL) or Internet Protocol (IP) address from which the photographs originate from. Once there is a set of pictures, other metrics can be used.
  • Such other metrics include facial recognition, clothing on persons detected as being in the captured images, the time difference between photographs in a given set, the location of where the images in the set where the images were captured, or common text that was identified from the image. Any of these metrics can be applied to identity recognition and/or classification, where a recognition signature or other recognition information is determined for a person in an image.
  • FIG. 6 is a block diagram illustrating a Double Bind technique for recognizing persons in a collection of pictures, under an embodiment of the invention.
  • Image data 510 from a captured image may be processed by first applying one or more facial recognition process 520.
  • Facial recognition algorithms suitable for an embodiment such as described with FIG. 6 are described elsewhere in this application, including with FIG. 3. While face recognition does not need to be performed first, it does include face detection, so as to be informative as to whether even a person exists in the image. If no person is detected, none of the other processes described in FIG. 6 need to be performed.
  • a face detection technique such as described in FIG. 3, is performed on each photograph in the collection, individually. Then, for every detected face, a facial visual signature v,- is calculated as described elsewhere, including with FIG. 3. The visual signature v / is used as one of the information sources.
  • the clothing information is used as another source of information. Accordingly, an embodiment provides that a clothing recognition process 530 employs a method such as described by FIG. 5 may be used to generate recognition information based on the clothing of the person.
  • time information 540 is contained as metadata with the image file, and it includes the creation time when the image was first captured.
  • the time/date can be obtained from the header (EXIF) of the JPEG file.
  • a time vector (ti) is a scalar that represents the time that the photograph is taken.
  • a time difference for two faces can be calculated as
  • processes described above may be used to create a face vector (fi) 552 , a clothing vector (ci) 554, a time vector (ti) 556, and a location vector (Ii) 558. Any combination of these multiple sources of information may be used independently, or in combination (e.g. "Double Binding") for purpose of determining identity or other identifiers of persons.
  • location information some digital cameras, including those that are provided as part of cellular telephonic devices, have started to include location information into the headers of their images.
  • This location information may be derived from GPS data, if the device is equipped with GPS receiver.
  • the location information may be determined from base station information when the device captures images.
  • location information may be determined in terms of longitude and latitude, particularly when the information is from a GPS device.
  • the location information 558 (//) is also calculated for every image in a collection. This vector contains the longitude and latitude information in scalar forms.
  • Programmatic clustering refers to use of programming to sort, categorize and/or select images from a larger set.
  • images are clustered together for purpose of facilitating users to assign correlation information to the images.
  • clustering images with a common individual for purpose enabling a user to tag all the images of the cluster with a name of the person in the images. This allows the person to tag the name of a person whom he or she has a lot of collections of with just one entry.
  • Clustering may be performed based on characteristics of the image file and of the contents of the image (e.g. recognition signatures and information).
  • the time and location information are used to group the photos to clusters (i.e. events).
  • the clusters are then used for identity recognition.
  • Two pictures (i, and j) are declared to be in the same directory, if:
  • the algorithm starts comparing the faces on the captured images. As an example, the algorithm may perform the following comparison while comparing two faces face m, and face n:
  • the difference vector is used as an input to the recognition algorithm.
  • the difference vector is used to asses the distance between two samples.
  • a K-Means algorithm can be used for clustering.
  • a modified K-Means algorithm can be used.
  • Programmatic clustering has applications beyond usage for enabling individuals to specify names, email addresses and/or other correlation information.
  • programmatic clustering such as described enables programmatic selection of a set of images for any purpose. As such, it provides an organization tool for enabling individuals to sort and select through images to a degree that is more sophisticated than directory and date sorting available today.
  • unsupervised clustering can be used to select sets of images from a larger collection or library.
  • An input to the algorithm is a list of detected faces (identities). For each identity, the system can calculate and/or determine any combination of recognition signature, clothing signatures, time stamp, and event cluster identifier.
  • the first step to such clustering is a distance matrix construction.
  • clustering is applied on the distance matrices.
  • the algorithm calculates a similarity matrix.
  • Each (ij)th entry of this matrix is the distance of identity i and identity j.
  • Such a matrix is symmetrical.
  • the distance between the identity i and j is a function of the following parameters:
  • One technique provides for an algorithmic traversal through every i and j in order to calculate the Distance(ij) between the identities i and j. After all i and j are traversed, the
  • a clustering algorithm may be based on a distance matrix.
  • An applicable algorithm has three major inputs: (i) Distance Matrix; (ii) Distance threshold, corresponding to a threshold to define when two identities can be put into the same Cluster(k), and (iii) Max
  • an algorithm applies a greedy search on the Distance Matrix.
  • STEP -1 the elements of the Distance Matrix are sorted in an ascending order of total sum of distances to the Closest N (a configurable constant) identities. This list is called the traverse list. This way, the algorithm traverses the identities that are closest to other identities.
  • STEP-2 The algorithm traverses identities in the order given in the traverse list. For the next identity i in the traverse list, the algorithm applies the following steps:
  • STEP- 2.0 if identity i is not already in a cluster, start a new cluster (call it Cluster(k)), and put i in this cluster, and Proceed to STEP-2.1. Otherwise stop here, and go to the next element in the traversal list.
  • STEP-2.1 Order all the identities with their distance to identity i (ascending order).
  • the output of STEP-2 is a list of clusters that are potentially quite densely clustered, due to the order that the lists are traversed.
  • STEP-3 Do a final pass on the clusters, and calculate the within-cluster-distance of each cluster. Then, order the list of the clusters using the within-cluster distances. This way, the clusters are ordered by their correctness-confidence.
  • One inference that may be used is that people in the cluster are more likely to be the same as the within-cluster distance. This is the order as the clusters are presented to the user.
  • the clusters can be
  • the clusters can be ordered by a combination metric of cluster size and their within-cluster-distances.
  • the system starts with some training face samples.
  • a system matches each image containing a face with the training sample using the distance metric d mn as described above.
  • a nearest neighbor classifier can be used for this purpose.
  • an n-nearest classifier can be used.
  • Other embodiments can use Neural Networks, Support Vector Machines, Hidden Markov models.
  • the identities are clustered within each photo cluster (i.e. event), then the identities from multiple events are matched together. For this, only the face information is used, since people tend to change their clothes between different events. If the face vectors 552 of two identities in different clusters look very similar, i.e. ⁇ f is smaller than a threshold T, then the clusters of those two faces are assigned to be the same identity.
  • recognition techniques including Double Binding, on digitally images that are not explicitly clustered.
  • the faces in two different photographs are clustered using a distance metric.
  • a distance metric may be used that corresponds to a combination of four different measures.
  • P(m,n are same identity) P(m,n same identity
  • conditional probabilities are pre-computed using training sets. Then a Bayesian belief network may be constructed among all probabilities between every face m and n. This network uses these probabilities to assign groups of same identities. The groups of identities are provided as an output.
  • relationship inference In addition to the various processes, and to Double Binding, another separate technique for recognizing people is relationship inference. Relationship inference techniques rely on the statistics of photographs providing implicit prior information for face recognition. For example, friends and family members usually tend to appear in the same photographs or in the same event. Knowing this relationship can greatly help the face recognition system to reject people who did not appear in some particular events.
  • the relationship inference can be implemented by constructing the singleton and pair- wised relationship potentials of the undirected belief network.
  • the singleton potential can be defined as the probability of the particular person appeared in a cluster or collection of images (e.g.
  • a virtual "photo album” in practice it can be computed by counting how many times this person's face appeared in the labeled ground truth dataset, and, optionally, plus the total mass of "prior experience” that we have.
  • the pair-wised potentials for the relationship between this particular person and other people can be defined as the probability of this person appeared together with other people in the same picture or the same event.
  • the standard belief propagation algorithm is then applied to compute the posterior probability of the face similarity to each identity.
  • the final recognition result is iteratively updated by gradient decent based on the posterior probability.
  • Generating a recognition signature or other recognition information may quantitatively identify a person in an image, but subsequent use of that information may require correlation. Examples of correlation processes include identity assignment (either manual or programmatic), as well as clustering.
  • recognized persons may be correlated to identities through a combination of programmatic assistance and manual input.
  • FIG. 7 illustrates a method for performing such a correlation, under an embodiment of the invention.
  • image files that are deemed to contain the same person are clustered together programmatically.
  • a clustering algorithm such as K-Means clustering can be used to group the similar faces.
  • a greedy clustering algorithm can be used, where each face feature is grouped with up-to n other face features that are closer than a difference threshold.
  • step 720 once the groups of faces are determined, the user is asked to assign identities (names) to the groups of faces.
  • identities names
  • the address book of the person can be downloaded from either the person's personal email account, or from applications such as
  • step 730 the correlation information is stored for subsequent use. For example, subsequent retrieval of the image may also include text content that identifies the individual by name. Alternatively, of other image files are captured in which the face is recognized as having the same recognition signature as the individual in the cluster, the identity of the individual is automatically assigned to the person in the image.
  • FIG. 8 illustrates an embodiment in which clustering of images is performed programmatically. An embodiment such as shown by FIG. 8 may be a result of implementation of a method such as shown by FIG. 7.
  • a programmatic module or element may programmatically cluster images in which persons are recognized to be the same. Once recognition clustering is performed, identity assignment and correlation may be performed manually, such as through OUTLOOK or other software.
  • names are loaded from an address book on one side (left in the example above), and the images are shown on the other side. The user provides input for matching the photos to the names.
  • a distributed training framework is used, where some of the address book items are automatically filled using the previously trained email addresses that are kept in a server.
  • recognized persons may be correlated to identities through a training process requiring more manual input and less programmatic assistance.
  • the user provides some number of examples for each person that they want to train the system to correlate and possibly recognize by identity.
  • the training faces may be provided to a programmatic module, such as described with FIG. 12.
  • the module may either determine the recognition signature for persons appearing the set of training images, or recall the recognition signature (if already determined) from a database, table or other programmatic component.
  • a system such as described in FIG. 12 may analyze all images for which no recognition has been performed for purpose of detecting persons and determining recognition signatures for detected persons.
  • the determined signatures may be programmatically compared to signature from the training set. Matches may be determined when determined signatures are within a quantitative threshold of the signatures of the training set. Thus, matches may not be between identical signatures, but ones that are deemed to be sufficiently close. The user may match the people to email addresses, or other
  • correlation between recognized persons in images and their identities may be established through a combination of unsupervised clustering and supervised recognition.
  • the unsupervised clustering may group faces into clusters as described above.
  • the results are shown to the user.
  • the user scan the results for purpose of correcting any mis-groupings and errors, as well as to combine two groups of images together if each image contains the same identity.
  • the resulting grouping may then be used as the training set to a supervised recognition algorithm.
  • the supervised recognition is then applied as provided in other embodiments.
  • a search algorithm may include a search of images carrying text that match or are otherwise deemed to be adequate results for a search criterion. Accordingly, an embodiment provides that individual images of a set are tagged and indexed based on recognized text contained in those images.
  • one embodiment may also filter what text is recognized, based on an understanding of context in which the text of the image appears.
  • a search on a specific word may provide as a result a set of images that have that word appearing in the images.
  • a search algorithm such as described may be implemented as an additional process to an existing image search algorithm, for purpose of enhancing the performance of the search.
  • Context and meaning for detected and recognized words may play an important part in a search algorithm.
  • the meaning of the text in the image can be derived from the text tag, possibly in combination with other sources, which can include: (i) other tags extracted from the image, (ii) the image metadata, (iii) context of the image such as web links pointing to it, directory information on the user file system, file name of the image, content of the web page where the image is displayed, (iv) external knowledge sources such as dictionaries, natural language processing software, and (v) input from the user.
  • the interpretation can then be used to enhance the relevance of the search based on the text found in the image.
  • related entities can be derived from the text, including: (i) orthographic variations and corrections, possibly based on a spell-checking algorithm, (ii) semantically related words which can broaden the scope of the search query, and (iii) related concepts, products, services, brand names, can be derived from the words to offer alternative search results.
  • Text detection and recognition is applied on each input image. These images could be either on the user's computers, or can be lying anywhere on the internet. Text detection finds the locations of the text in the images. Text recognition uses a normalized image around the detected regions and determines the text that corresponds to the region.
  • FIG. 9 provides a description of how text detection and recognition may be performed in a larger context of handling text in captured images. While detecting and recognizing text in images is useful for searching images, other uses for a method of FIG. 9 exist. Among them, the appearance of text may enable users to select portions of the image (as will be described in FIG. 18 and elsewhere) in order to perform on-the-fly web searches, or to be pointed to a specific network location (e.g. web site), or to be presented additional information about the text or text carrying object.
  • a specific network location e.g. web site
  • FIG. 9 a basic method is described for recognizing and using text when text is provided on objects of an image, under an embodiment of the invention. Further, as will be described, not all text encountered in an image is useful. For example, text appearing on a slogan of a t-shirt worn by a person in a picture may not be of use, but text appearing on sign, indicating the name of a business may have commercial use in an online library. Embodiments of the invention further enable programmatic distinction of when text appearing in images is relevant or useful, and when it is best ignored.
  • step 910 an image may be analyzed to determine the presence of text.
  • the text may appear on another object.
  • This step may be performed independently of, or at the same time as analysis of the same image for facial or physical characteristics of persons.
  • text detection can be performed using a two-stage technique.
  • the technique may include training stage, and a testing (detection) stage.
  • the training stage is used to train a classifier on how the text looks. For this reason, a training set of text regions and non-text regions are provided.
  • the algorithm starts with a list of hypothesis feature vectors fj, and their weights ⁇ ;.
  • an Adaboost algorithm may be trained to specify which of the features to use and how to combine them.
  • f;'s involve lots of edge features in an image.
  • histograms of the intensity, gradient direction, color information and intensity gradient of the image can be used.
  • Each feature f; produces a weak classifier, and the final classifier is a weighted version of this classifier as given as follows:
  • the strong classifier H is optimized on values of ⁇ ;. In other words, training stage learns the optimal combination of the features.
  • the testing (detection) phase applies these features for every hypothesis of pixel location. If the strong classifier result H is above a threshold T, the region is identified to be a text region, , with an associated set of properties such as orientation, confidence, height, and slope.
  • FIG. 1OA provides individual examples of features, provided as block patters, provided for purpose of detecting the presence of text in an image, under an embodiment of the invention.
  • the premise in use of block patterns is to provide blocks with contrasted regions adjacent to un-contrasted regions, and vice- versa.
  • a set of individual block patterns 1010 are selected to represent shapes or features of individual letters, numbers or other characters. In this way, the block patterns 1010 serve as markers for text, in that when a block diagram is detected, the potential for the existence of text is present.
  • the window may be scanned for one or more of the block patterns 1010.
  • a training algorithm such as Adaboost
  • a determination of whether a given block pattern exists in an image may result in a statistical based value, which when Adaboost is a training algorithm.
  • Binarization refers to conversion of color or shaded text into binary form (e.g. black and white) to, among other reasons, enhance the performance of the OCR.
  • a binarization algorithm may be applied on regions of the image detected as having text.
  • an adaptive binarization algorithm can be applied. For every pixel, the mean ( ⁇ ) and standard deviation ( ⁇ ) of a window around that pixel is calculated. The pixel is binarized accordingly with a threshold.
  • an unsupervised clustering algorithm is used adaptively on the color image (with or without gray level conversion).
  • a K-Means algorithm can be used with a k value of 2. This algorithm would divide the region into multiple, possibly overlapping regions including: dark text foreground, light text background, light text foreground and dark text background.
  • text stretching may be applied to the detected text.
  • text stretching a portion of a word is detected.
  • a programmatic element knows that additional text may be located in the image along a path or line defined by the text already detected.
  • FIG. 1OB illustrates how detection results, in a portion of the term "animal”, and stretching identifies the remainder of the term.
  • FIG. 1OC illustrates how a portion of the term "Boutique" is located, and because part of the word is found, the system knows that the remainder may also be present. Both examples provide an example of a linear path for which image data may be inspected for the presence of text.
  • connected components of the detection regions are found.
  • the components are grouped together by relevance to their distance in between, to their shapes and heights.
  • a slope of grouped connected components is calculated by fitting a line to the centers of the grouped components.
  • a least square fit, or a weighted least square fit algorithm can be used for this purpose.
  • the text may be extended in the direction of the slope in both sides.
  • the text box is extended in the direction of the slope for this reason. The text is not extended if the regions beyond the detected text do not match text-like attributes
  • the text can then be re-binarized based on global attributes of the text region, including average size of the letters, spacing, foreground color, type of font used, and possibly a first attempt at recognizing the text using OCR (see section below).
  • the text regions can then me merged into complete lines of text based on their alignment with respect to each other.
  • the regions can then be corrected for orientation, skew, slope, scale factor and contrast yield and image containing black text on white background, of a consistent average size, and aligned horizontally, which is the preferred format to perform OCR.
  • FIG. 1OD illustrate specific examples where detected text appears in a skew or slanted orientation, and then is processed so as to be re-oriented to be more planar with respect to the two-dimensional orientation of the image.
  • step 920 provides that the detected text is recognized.
  • the recognition information generated from recognizing such text may be in the form of a set of alphanumeric characters. More than one set may be recognized for the same image, with each set representing guesses of characters or numbers with various levels of confidence.
  • the detected and binarized text region is used as an input to an OCR algorithm. Any OCR algorithm and package might be used for this purpose.
  • the output of this stage is text that corresponds to the detected text region, along with a set of attributes which are typically produced by the OCR, including but not limited to: font, alternative candidate letters, bold/italic, letter case, character confidence, and presence of the word in the OCR dictionary. These features may be used to assess the confidence in the output text.
  • text detection and OCR can be used jointly, for example using an iterative process where the text detection first performs a crude segmentation of the image, and OCR then identifies likely text regions. The likely text regions are passed to the text detection and normalization to be refined, and sent back to the OCR as many times as necessary to obtain a final text recognition result.
  • multiple binarization outputs can be produced using different binarization thresholds, and the output with the most OCR confidence can be used as the main output.
  • step 930 the text is interpreted, so as to provide context or meaning. For example, when recognition yields a string of characters, step 930 may interpret the string as a word or set of words. In performing this step, one embodiment may utilize confidence value
  • a dictionary assist can be used.
  • the words that are not in a dictionary can be eliminated/corrected using the dictionary.
  • a finite automate state machine can be used in order to implement the dictionary.
  • Another embodiment may use language modeling techniques such as n- grams. These techniques would calculate the probability that a letter is followed by (n-1) other letters. For every letter i (1;) in a word, the following probabilities would be calculated:
  • the word probability can be calculated by the multiplication of the probabilities of every letter in the word. For instance, the probability of the word WORLD, is given as:
  • P(WORLD) P(W
  • the set of features extracted from the OCR can be combined using a regression or classification technique to compute the probability of the word sequence to be correct.
  • An instance of this method uses a linear classifier, which linearly combines the set of numeric values associated with each feature to produce a confidence score.
  • This linear classifier can be trained from data using Linear Discriminate Analysis. Non-linear classifier such as Neural Networks or Support Vector Machines can also be used.
  • the confidence score can then be mapped to a posterior probability of being correct using a ROC curve computed from training data.
  • each OCR engine is given a text detection output, possibly using different text detection and normalization parameters, and produces its own hypothesis or set of hypotheses as to what the text is, and an associated confidence measure. These outputs are then
  • model combination techniques include: simple voting, confidence voting, ROVER and Bayesian Model Combination (BAYCOM).
  • BAYCOM Bayesian Model Combination
  • a large sign saying "WELCOME TO SAN FRANCISCO" on a photograph is relevant in determining the location of the event.
  • a small street sign saying "NO PARKING" in the background of the picture might not be relevant to any search query.
  • several cues can be used, including but not limited to: the semantics of the text, the text location, size, contrast, and sharpness of focus. Dictionaries and thesauri can be used to determine the possible semantic classes the text belongs to (for example a city database is useful in determining that "San Francisco" is a city name, hence relevant as a location tag).
  • images may be tagged, indexed or otherwise associated with metadata that corresponds to the text contained somewhere in the image.
  • an index or other form of tag representing recognized words may provide a searchable structure in which search criteria is matched to images based on text carried on objects in those images.
  • Text correlation also lends itself to applications that utilize the text recognized in the images. Once the text is found in each image, that image is tagged (indexed) with that tag. Additional techniques (such as described below) may be used to create more tags in each image and neighboring images.
  • One such embodiment provides for an extrapolation technique, which can be used to find tags and relate those tags to different characteristics of other images, including text contained in those other images. For instance, if a text content "San Francisco” is programmatically identified from an image, then an embodiment may provide for the determination and association of additional relevant tags to the recognized text content. For example, in the case where the recognized text is "San Francisco", related tags associated with that term include “Bay Area, California", and "USA”.
  • One text extrapolation technique may provide for a build of a database, table, or other relational data structure which relates a recognized text with other words, names or phrases.
  • a database may be built which associates individual words in a library of potentially recognized words with other relevant words.
  • a database may be provided which relates potentially recognizeable words with one or more other relevant words.
  • a database may be built based on locations, restaurants names, hotel names, yellow pages.
  • Tag spanning adds an additional dimension of relation when correlating text recognized from images to other image files.
  • a text or other tag found on a particular image may be applied to other images that are relevant to that particular image, where such relevance is based on a parameter or factor other than recognized text content. For instance, if the text San Francisco Hilton Hotel was found in one of the images, the same tag can be assigned to pictures that were taken around the same time-frame.
  • the time-stamp information can be obtained from the EXIF (header of an image file containing metadata) of the image.
  • the same tag can be applied on pictures that were taken at a similar location.
  • the location (GPS) information can also be obtained from the EXIF of the image.
  • a database of spannable words may be constructed, where spannable words are meant to include words that can be determining to have a meaning or content to them. For instance, the word “the”, or "Budweiser” may be considered not spannable, whereas the location names, or proper names of businesses (such as restaurant names and hotel names) are spannable.
  • Tag spanning assures that all relevant images are tagged with extracted tags.
  • tag spanning techniques are employed in connection with programmatic intelligence for determining what words are spannable.
  • FIG. 11 illustrates a technique in which a detected and recognized word in one image is then spanned across a set of images for purpose of tagging images in the set with the recognized text, under an embodiment of the invention.
  • step 1110 text is detected from a given image in a collection of images. No determination of a set of images may yet be made for purpose of spanning.
  • step 1120 provides that a determination is made as to whether the text provides a relevant tag of
  • The, outcome of the determination may be based on the meaning of the detected text, as well as other factors that may include any of the following: (i) an identification or understanding of the object that carried the text in the image; (ii) the size or placement of the text in the source image; (iii) the format or font of the recognized text as it originally exists in the image; (iv) other information recognized or determined from the source image, including metadata such as the time of the image being captured of the location where the image was captured, as well as recognition of people or other objects in the image.
  • step 1120 determines whether the text does not provide a relevant tag. If the determination in step 1120 is that the text does not provide a relevant tag, then step 1125 provides that the detected and recognized text is ignored, and other text from the same image or other images in the collection are used. As an alternative, the text can be tagged in the image, but not recognized as a spannable text.
  • step 1130 provides that a determination is made as to whether the text is spannable.
  • Spannable text corresponds to text that is (i) carried in one or more images of a set, and (ii) is relevant to other images in a set of images as a whole.
  • text describing or indicating a location in one image of the set can be relevant to all images in the set in that it indicates the location where all images in the set were taken, regardless of whether the particular text actually appears in anymore than one image in the set.
  • spannability of text is determined using the relevance determination, including applying recognized text to semantic classes such as locations (e.g. landmarks, cities, countries) or events (wedding, party, holiday). Relevance scores may be generated based on a threshold is applied to the relevance score of the text to determine whether or not to use it for spanning.
  • step 1140 provides that the detected text is ignored for purpose of spanning. However, the text may still be used to tag the source image as a relevant text.
  • step 1150 provides that a set of images are determined from the collection that can be spanned by the identified text.
  • the grouping of images from the collection into the set may be based on a factor other than text content. Rather, images in the set may be determined to be relevant to one another based on some other characteristic of the images.
  • the factor that determines relevance among images in the collection is at least one of (i) the time when an image was captured, and (ii) a location where the image was captured, any spannable tag is spanned along a timeline or duration of time. Given an image with a spannable tag, the system looks
  • the weight is a Gausian G(t,tO,sO) where t and tO is the timestamps of the second image and the original image (image with tag), sO is the standard deviation for degrading.
  • G(t,tO,sO) where t and tO is the timestamps of the second image and the original image (image with tag), sO is the standard deviation for degrading.
  • a slight modification includes a cut-off if the image is beyond n*s ⁇ of the original image ( i.e.
  • the spanning confidence can be combined as a function of two confidences and the timestamps of two source images.
  • a linear ramp weighting can be applied instead of a Gaussian fall off.
  • a set of images in a collection may be tagged with metadata that corresponds to the detected and recognized text from just one image in the set. Additionally, an embodiment provides that the detected and recognized may be extrapolated, and extrapolated data may be spanned across the identified set of images in the set. Thus, if an image contains text referencing the name of a well-known business or establishment in a given city, the text may be extrapolated to the name of the city, the type of business or establishment that the text identifies, and alternatives to the particular business or establishment made by the identified text. So were all of these text items may be tagged on each image identified in step 1150. [170] SYSTEM DESCRIPTION
  • FIG. 12 illustrates a system on which one or more embodiments of the invention may be performed or otherwise provided.
  • a system such as described by FIG. 12 may be implemented in whole or in part on a server, or as part of a network service.
  • a system such as described may be implemented on a local computer or terminal, in whole or in part.
  • implementation of a system such as shown requires use of memory, processors and possibly network resources (including data ports, and signal lines (optical, electrical etc.).
  • an embodiment such as shown by FIG. 12 may be used for purpose of analyzing images and recognizing objects, as well as building an index based on recognition of objects in the images.
  • a system includes image analysis module 1220 that analyzes images that recognizes objects appearing in the images.
  • the image analysis module 1220 is configured to generate recognition data of different types of objects appearing in individual images for purpose of enabling the recognition data to be indexed. Indexing enables functionality such as search and categorizing or sort.
  • the image analysis module 1220 is configured to generate recognition data of different types of objects appearing in individual images for purpose of enabling the recognition data to be indexed. Indexing enables functionality such as search and categorizing or sort.
  • An Identifier Information Index 1242 may use correlation information as its index data element.
  • the correlation information may be in the form of text data, such as the proper names of person in s recognized, toward determined from recognized tax carried on an object, or an identification for what an identified object is.
  • a signature index 1252 uses numeric or quantitative signature data that substantially uniquely identifies a person or object.
  • signature index in 1252 may store data that will enable a determination that two separate digital images contain the face of the same person, but information corresponding to the name or identity of the person may be maintained elsewhere outside of this index.
  • the use of separate indexes to maintain identifiers based on correlation information and quantitative recognition signatures is a design implementation to facilitate numerous types of functionality, including text searching for images, image search for images, and similarity or likeness searches (described in more detail below).
  • Other implementations may also provide for ID Information index 1242 and signature index 1252 to share information, or otherwise be linked so that recognition signatures and information are provided with identities.
  • Image analysis module 1220 includes a person analysis component 1222, a text analysis component 1224, and an object analysis component 1226.
  • the person analysis component 1222 may analyze image data from a given image file for purposes detecting and generating recognition any person appearing an image. As described elsewhere, the detection and recognition of persons may be based on the presence of facial features, clothing, apparel, other persons recognized or otherwise known to be in the image, or other recognitions of persons made from other images related (by, for example, time and/or geography) to the one being analyzed. When a person is recognized from an image, recognition information corresponding to in identifier of that person may be outpurted by the image analysis component 1220.
  • the identifier of the person generated from the person analysis component 1222 is a recognition signature 1253, meaning the identifier substantially uniquely identifies the person from other persons.
  • the recognition signature 1253 may be supplied to a signature index 1250.
  • the person analysis component 1222 may also be configured to retrieve correlation information corresponding to the identified and/or recognized person of a given image. This correlation information may, for example, be the proper name of the individual.
  • the person analysis component 1222 may have access to a correlation database (not shown) which provides the proper name or identifier of the person, or the information may come from knowledge store 1218.
  • user input may be used to determine the identifier of the person recognized from a given image.
  • Other examples of the identifier of the person may correspond to a class or group of the person.
  • the correlation information may be in the form of a person identifier 1233 that is supplied to this ID Information Indexer 1240.
  • the text analysis component 1224 detects the presence of text in an image under analysis. As described with FIG. 9, text analysis component 1224 may make a determination as to whether the text is material and/or relevant to the image under analysis. Furthermore, as described with FIG. 11, the text analysis component 1224 may perform functions of extrapolating and spanning. An output of the text analysis component 1224 is text object information 1235. This information may correspond to words or other text data that is recognized from the image under analysis, or extrapolated from another recognized word. When spanning is used, the text object information 1235 may be associated with the image under analysis and any other image determined to be in a relevant set of the image under analysis.
  • the object analysis component 1226 may perform detection in recognition of objects other than persons or text. Examples of other objects that can be recognized include: landmarks, animals, geographic localities, structures by type (e.g. church or high-rise) or by identity ( e.g. Taj Majal), and vehicles (e.g. by type or by manufacturer).
  • the object analysis component 1226 may employ different recognition processed for different types of objects, as well as for different types of environments for which the recognition is to be applied from.
  • the recognition of objects in real- world scenes is a complicated task for a number of reasons. Some of the issues presented with recognizing objects include intra-category variation, occlusion, three-dimensional pose changes, and clutters.
  • One approach for recognizing certain types of objects is to model objects as constellations of localized features.
  • a set of training images is collected for each type of object that is to be recognized. Once the training set is collected, a corner detector is applied to obtain the salient local features for each object.
  • 5 /8/20Q6 representation of these local features can be the filter response from, for example, a Gabor wavelet, SIFT, spin image, or other recognition technique.
  • the local features can be further condensed by clustering.
  • the representation of local features is in-sensitive to small changes of scale, pose, and illumination.
  • the affme-invariant features can also be computed to handle large pose variation.
  • the recognition process simply computes the similarity between the local features for each registered object and the local features for the given test images.
  • the shared feature clusters activated by the local features of the test images can be used to vote for the object hypothesis.
  • the object recognition process can be integrated with segmentation and evaluated by the belief network jointly and efficiently.
  • the ID information Indexer 1240 may receive correlation information, such as in the form of text data that identifies what a recognized object is. For example, a picture with the landmark of the Eiffel Tower may be recognized and correlated to the proper name of that landmark, in this data may be supplied to the ID information indexer 1240 as object identifier 1237. At the same time, a quantitative or numerical representation of the landmark may be supplied to the signature indexer 1250.
  • each of the indexers supply their own respective index.
  • the ID Information Indexer 1240 submits ID index data 1245 to the ID Information Index 1242.
  • the Signature Indexer 1250 supplied Signature Index Data 1255 to the Signature Index 1252.
  • ID index data 1245 and Signature Index 1252 enable specific types of search and retrieval operations.
  • ID index data 1245 enables retrieval of images based on text input.
  • a user's search criteria of a proper name will return images that have been recognized to containing the person with the same name. This operation may be completed using the ID Information Index 1242 as a source.
  • a user's search criteria of an image of a face may return images containing the same face.
  • This operation may be performed by (i) recognizing the face in the image that is to serve as search input, and (ii) retrieving an image with the same or equivalent recognition signature using the Signature Index 1252 as a source.
  • Similarity matching may be used in comparing this signature of an input image with the signature of other images stored with signature Index 1252 for purpose of determining similar recognition signatures. Similar recognition signatures may yield any of the following: (i) individuals who look-alike based
  • the image analysis module 1220 may receive image input from a variety of sources.
  • image analysis module 1220 is part of a network service, such as available over the Internet.
  • image analysis module 1220, ID information indexer 1240, and signature indexer 1250 may be server-side components provided at the same network location, or distributed over more than one network location.
  • one or more of the indexes may be provided as a separate service, and at a separate Internet web site than the image analysis module 1220.
  • image analysis module 1220, as well as any of the indexers may be local or client side components.
  • images may be provided from an image capturing device, such as a digital camera, or through user-controlled devices and/or client terminals.
  • Specific types of clients include image capturing and/or display applications that run on, for example, laptop and desktop computers, and/or combination cellular phone/camera devices.
  • the location of the individual components may influence the type of input that can be handled by the system.
  • the sources for images that are indexed maybe programmatic, manual, or a combination.
  • a manual source 1284 may be provided to enable users to manually enter image input 1204.
  • Image input 1204 may correspond to images submitted by a user for recognition and indexing, as well as images that are intended to be input for purpose of searching or similarity matching.
  • the image input 1204 may correspond to (i) one or more image files transferred from a digital camera device (e.g. wireline transfer from digital camera to desktop computer, or cellular transfer (via email or Multimedia Messaging Service (MMS)) from combination device to the desktop computer) , (ii) receive and opened via e-mail or other network service, (iii) downloaded from the Internet, or (iv) designated as being in a folder residing on a machine used by the user. In the latter case, the folder may be part of a local library 1247 or part of a network library 1249.
  • image input 1204 may also be provided as responsive input in the form of a selection of an object in an objectified image rendering 1910 (see FIG. 18 and FIG. 19).
  • the manual source 1284 may also provide text input 1206, that serves as correlation information
  • text input 1206 may correspond to the proper name of a person, which can then be used with the person analysis component 1222.
  • the user may link text input 1206 with image input 1204.
  • Such link information 1209 that links text input 1206 with image input 1204 may be carried as metadata, and supplied to, for example, the ID information indexer 1240.
  • knowledge store 1218 Another source for text input 1206 is knowledge store 1218.
  • knowledge store 1218 may correspond to an address book, such as provided through OUTLOOK.
  • knowledge store 1218 On a network service, knowledge store 1218 may correspond to a directory of names, or object identifiers.
  • programs such as OUTLOOK may carry pictures of contacts, and the picture may be carried as image data to the signature indexer 1250.
  • the text input may be used for correlation purposes. For example, an unrecognized image may be given an identifier in the form of text input 1206, either from the user or from the knowledge/ID store 1218. The identifier may be carried to the ID Information Indexer 1240, where it is indexed with the recognition signatures and/or information generated from the image.
  • Another use of text input 1206 is to provide feedback as to whether recognition is correctly done for a given person, text or object.
  • programmatic sources 1294 may be employed in some embodiments for purpose of obtaining image input 1220.
  • Programmatic sources 1294 include programs or applications that gather images substantially automatically.
  • the programmatic source 1294 is used to update indexes maintained by an online service, such as an image search engine available to Internet/network users.
  • the programmatic source 1294 may include a crawler 1292 that crawls web sites for images, or crawls through directories of users for images.
  • users of the service may access submit image files or folders, and the programmatic source sequences or otherwise prepares the image files for processing by the image analysis module 1220.
  • the programmatic source 1294 may be a local or client side agent that retrieves images automatically (or with some user input) for use by image analysis module 1220.
  • image analysis module 1220 retrieves images automatically (or with some user input) for use by image analysis module 1220.
  • programmatic source 1294 manual source 1284, and the location of those and other components of a system described with FIG. 12.
  • any image input 1204 may be processed, as an initial step, to determine whether that particular image was previously analyzed and recognized by image analysis module 1220.
  • a component labeled new image check 1208 makes an initial inspection of an image file to determine whether the image file has been handled by the image analysis module previously. The initial inspection may be performed by way of an analysis of metadata contained in a header of the image file or otherwise associated with the image file. From the in one implementation, new image check 1208 extracts metadata 1223 from the header of the submitted image file, and checks the extracted metadata against a picture ID store 1225. If the image file has never been analyzed before, metadata 1223 is stored in the picture ID store 1225.
  • new image check 1208 omits forwarding the image file to the image analysis module 1220. In this way, a response 1229 from the picture ID store 1225 results in either the image file being ignored/discarded (for processing purposes), or analyzed.
  • FIG. 13 illustrates person analysis component 1220 in greater detail, under an embodiment of invention.
  • a premise for performing recognition is that a substantial number of markers, other than face appearance information, are present in user photographs.
  • a system such as shown is configured to exploit these non- facial markers (or other recognition clues) for purpose of improving the recognition performance of the system as a whole. Some of these markers, such as clothing and apparel, have been described in detail in other embodiments.
  • FIG. 13 illustrates different techniques, image markers, and information items in order to assemble recognition signatures and information, as well as identity correlation.
  • a person analysis component 1220 may include a face detect component (“face detector") 1310, a metadata extractor 1312, a marker analysis module 1320, and a Content Analysis and Data Inference (CADI) module 1340.
  • Image input 1302 may be received by face detector 1310 and metadata extractor 1312.
  • the face detector 1310 may detect whether a person is present in the image.
  • the face detector 1310 may normalize image data of the detected person for use in recognition processes that are to be performed by the marker analysis module 1320. Normalized input 1311 may be provided from the face detector 1310 to the marker analysis module 1320.
  • the metadata extractor 1312 identifies metadata indicating creation time of the image input 1302. Time input 1313 is submitted by metadata extractor 1312 to the CADI module 1320.
  • the marker analysis module 1320 may comprise of several recognition components, each of which use a particular marker or characteristic to recognize a person.
  • marker analysis module 1320 includes a facial identifier 1322, and one or more of the following components: a clothing/apparel component 1324, hair analysis component 1326, a gender analysis component 1328, and a relationship analysis component 1329.
  • Relationship analysis component 1329 may (alternatively or additionally) be part of CADI module 1340, as it relies on inferences to an extent.
  • Each of these components may be configured to generate recognition information specific to a person detected from image input 1302. Recognition information from some of these components, including facial identifier 1322, maybe the form of a signature, with substantially uniquely identifies the person in the image input 1302. Other components, such as gender analysis component 1328, may only provide recognition information that is less granular in identifying person in the image input 1302, as compared to recognition signatures.
  • the CADI module 1340 may receive recognition information from each of the components of the marker analysis component 1320 for purpose of providing an identity and/or correlation to the person appearing the image input 1302.
  • facial identifier 1322 may provide face recognition information 1342.
  • Face recognition information 1342 may be provided in the form of a signature, which is uniquely or substantially uniquely identifying of that person.
  • the facial identifier 1322 independently or in connection with face detector 1310, may execute processes in accordance with methods such as described in FIG. 4 for purpose of generating recognition information based on the face of the person.
  • the clothing/apparel component 1324 may provide clothing recognition information 1344, as described with a method of FIG. 5 and other embodiments.
  • the hair analysis component 1326 may provide a hair recognition information 1346, including color,, length or hair style.
  • the gender analysis component 1328 may provide gender recognition information 1348.
  • relationship analysis component 1329 may provide relational recognition information 1349.
  • the marker analysis module 1320 communicates signatures and recognition information to the CADI module 1340, and the CADI module 1340 performs inference and correlation analysis to provide CADI feedback 1355 to the person analysis module 1320.
  • CADI module 1340 may receive the different recognition information and draw inferences that indicate whether the components of the marker analysis component 1320 are accurate.
  • the CADI module 1340 may
  • the CADI module 1340 may perform analysis of recognition information on more than one image, so as to perform context and inference analysis by identifying images as belong to an event, or to a photo-album, and having information about those other images ready.
  • the components of the marker analysis module 1320 may supply recognition information to programmatic or data elements that can use such information.
  • recognition information derived from each component of the marker analysis component 1320 may be generated and submitted to the indexer 1360, which then generates data for its index 1362.
  • the recognition information may be indexed separately from each component, or combined into signatures 1352.
  • signatures 1352 is a vector value based on vector quantities supplied by all of the components of the marker analysis module 1320, either before or after influence from the feedback 1355 from the CADI module 1340.
  • the index 1362 may store the recognition information from one or more of the components of the marker analysis component separately or additively.
  • the CADI module 1340 may provide recognition signatures 1353 for a given person recognized from the image input 1302. Such an embodiment enables the recognition information from the marker analysis component 1320 to be indexed separately from data that is affected by context and data inferences.
  • the recognition signatures 1353 from the processing algorithms of the CADI module 1340 may substitute for signatures 1352 from the components of the marker analysis component 1320.
  • the recognition signature 1353 from the CADI module 1340 may supply one recognition signature which takes into account recognition information from two or more components of the marker analysis component, as well as other factors such as event or photo-album determination.
  • information determined or extracted from either the marker analysis module 1320 or the CADI module 1340 may be provided as metadata with the image
  • this metadata 1356 may be provided with the actual image file 1366, so that recognition information and other information relating to recognition are carried with the image file.
  • the metadata 1356 may be provided with a metadata store that matches metadata (may include recognition information and signatures) with a given image file.
  • the header (EXIF) of an image file includes metadata that can be used in facilitating recognition.
  • This information may include creation time (time metadata 1313), corresponding to when an image was captured, although it can also include location information of where the image was captured through cellular base information and/or GPS information.
  • the time information 1313, as well as location information if provided, may be used by the CADI module 1340 to cluster in image provided as part of the image data input 1302 into a set. Such a set may denote that the image input 1302 is part of an event.
  • Two pictures (i, and j) are declared to be in the same event, if:
  • the CADI module 1340 may perform its analysis to provide the feedback data 1355 as follows. For a particular image with a time stamp scalar (ti), time difference for two faces between the image at hand, and an image known to the CADI module 1340 as having just previously been taken, can be calculated as
  • This difference vector can be used as an input in order to determine a probability that the recognition information 1342 from the facial analysis component 1322 is correct in its determination. For example, if the time lapse between successive images is small, the chances are more likely that the two faces are the same. For example, if the time lapse between successive images is less than a second, then the odds are high that two images are the same person (assuming the images are taken from the same camera). These indications may be carried quantitatively or otherwise in the CADI feedback 1355.
  • CADI module 1340 may group images together as being part of a photo-album, when a photo-album is designated by the use or determined from other information. For example, the user may submit the photo-album as a folder on his computer, or the CADI module may identify all pictures taken on a particular day, and maybe at a particular location, as belonging to the same photo-album. In such cases, statistical analysis is useful with respect to appearances.
  • factors that may be maintained and used by the CADI module 1340 in providing the feedback 1355 include: (i) some people tend to appear more frequently in the album, (ii) friends and family members, as well as certain groups of friends (for example, the photo owner's friends in Turkey) tend to appear in the same photographs; (iii) some people (e.g. husband and wife) usually stand close to each other in the photos.
  • the statistics can concern the same event (subset of the pictures which were taken within a certain short period of time): (i) an event photo usually tend to contain the same set of people (that are meeting, having dinner, taking a trip); (ii) in the event, some people may be appearing together (such as the people sitting at the same table in a restaurant).
  • other statistics can refer to a single photo. For example, the same person cannot appear twice in the same photo.
  • Clothing is another powerful marker which can aid identity recognition.
  • CADI module 1340 may also using the clothing recognition information 1344 in its feedback 1355.
  • clothing recognition information 1355 can be used to exploit the following dependencies: (i) people tend to wear the same clothing at an event, (ii) people possess certain easily recognizable items of clothing.
  • Appearance statistics can be used to fix some errors of the face and person recognition algorithms (as performed by the different components of the marker analysis module 1320). For example, based on the face information alone, uncertainty may exist as to whether a person next to "John" is John's wife, or a similar-looking person in Germany. In such a case, the appearance priors can be used to make an educated guess.
  • the various types of recognition information provided from marker analysis component 1320 may be used by the CADI module 1340 to generate identity/correlation information 1354.
  • the identity/correlation information 1354 may correspond to a proper name of a person, or alternatively be in the form of relational data that relates recognition information from one person to an image file and/or to other persons or objects that are determined to be relevant to the recognition and/or identification of that person.
  • the CADI module 1340 matches together the identities from multiple events. For this, only the face information may be used, since people tend to change their clothes between different events. If the face vectors of two identities in different clusters look very similar, i.e. ⁇ f is smaller than a threshold T, then the clusters of those two faces are assigned to be the same identity.
  • the CADI module 1340 may incorporate the various markers into a coherent probabilistic graphical model, which is able to perform complex reasoning in order to find the most likely identity assignments.
  • the appearance statistics are probabilistic in nature, and are captured well using probabilistic graphical models, in particular undirected models such as Markov Random Fields (MRF), also known as Markov networks.
  • MRF Markov Random Fields
  • a model may be formed based on a determination of a probability corresponding to how likely a person is to appear in any photo, or to appear in a photo during a particular event using single probabilistic potentials. These potentials model the likelihood of the person to appear in a particular photo or event.
  • the potentials can be estimated in practice by counting how many times a person appeared in a labeled ground truth dataset, and these counts can be extended by adding additional "prior experience" which we may have about person appearances.
  • the CADI module 1340 bases its determinations on input from the marker analysis module 1320.
  • the previously described face recognition engine can be used to provide beliefs about the identity of unknown examples; the potential counts can be obtained by adding these beliefs.
  • the relationships between several people can be captured using potentials over pairs or triples of variables, which assign likelihoods to all possible combinations of the variables involved.
  • the CADI module 1340 may also execute a reliable sex classification algorithm markers to constrain the set of possible matches for a person. Sex recognition can be performed by using training a classifier such as provided by the techniques of Adaboost and Support Vector Machine. The classification of sex by the algorithm is denoted as (si). [208] Additionally, CADI module 1340 may utilize hair color, length and style in providing the feedback 1355. For example, some people consistently maintain the same hair appearance, while others maintain the same hair appearance during an event. The hair can be extracted using a box in a pre-set location above the face box, as well as using an algorithm
  • This vector may be provided by the hair recognition information 1346 and compared to known information about hair in relation to pictures from a common event.
  • the CADI module 1340 may perform additional recognition through use of one or more "double binding" techniques. Recognition information from any combination of two or more components of the marker analysis module 1320 may comprise use of a double binding technique.
  • a grouping of images from an event are identified, using for example, time information 1313 and location information.
  • faces in images from a set of images correlated to an event may be compared to one another. For example, two faces face m, and face n may be compared as follows:
  • FIG. 14A is a graphical representation of the Markov random field, which captures appearance and co- appearance statistics of different people.
  • a simple instantiation of the MRF model to a domain instance with two images is shown in FIG. 14 A.
  • each rectangle represents an image in the album.
  • Each circle represents a variable Pi corresponding to the identity of the detected face in that place in the image.
  • This allows the person analysis module 1220 shown in FIG. 12 and FIG. 13 to capture face recognition information and appearance counts information in the same model. Additionally, pairwise (and possibly, higher-order) co-appearance potentials ⁇ (Pj,P j ) can be introduced to capture the likelihood that the respective people appeared together in this image.
  • the CADI module 1340 can perform probabilistic inference, so as to find the most likely identities which maximize the likelihood of the model.
  • the inference effectively combines the beliefs provided by the face recognition algorithm, and the beliefs derived from the appearance statistics. This inference can be performed very efficiently using standard techniques such as Markov Chain Monte Carlo algorithms, Loopy Belief Propagation, Generalized Belief Propagation and their variants, or Integer Programming.
  • the pairwise potentials can contain parameters, which specify how likely two particular people are to be seen in a particular image. If a separate parameter for each pair of people is used, the number of parameters available to estimate from a particular album grows quadratically with the number of people the album contains.
  • a more robust estimation scheme that can be performed by the CADI module 1340 would allow parameter sharing for groups of people. This can be accomplished by automatic clustering of the people into groups that tend to appear together, and using the use the same parameters for all people in the group.
  • an approach starts with using ground truth data in combination with face recognition information 1342 and possibly other recognition information from other the markers (e.g. clothing, sex and hair).
  • These results come in the form of recognition beliefs for each face in the dataset, and can be deterministic (if the example is labeled in the ground truth) or probabilistic (if the identity estimate is provided by the face recognition algorithm).
  • the beliefs can be added to obtain a vector with a different value for each person. This value corresponds to the likelihood of that person to appear in the image (the likelihood does not have to sum to 1, it can be normalized subsequently).
  • entire album can be represented as a person-image matrix, whose columns correspond to beliefs generated by the CADI module 1340 about the appearance of different people in the images. From such a matrix, what is extracted is information identifying groups of people that tend to co-appear in the same images. This can be achieved with matrix factorization techniques such as Latent Semantic Indexing, or Non- negative Matrix Factorization, or with probabilistic clustering techniques including, Naive Bayes clustering, and Latent Dirichlet Allocation. As a result of these techniques, several clusters of people may be identified by, for example, the CADI module 1340. In the pairwise potentials, they will share the same pairwise parameters accounting for interaction within the group, and for interaction with other groups of people.
  • matrix factorization techniques such as Latent Semantic Indexing, or Non- negative Matrix Factorization
  • probabilistic clustering techniques including, Naive Bayes clustering, and Latent Dirichlet Allocation.
  • Double-binding techniques employed by the person analysis module 1220 may also incorporate clothing information as a primary factor in determining or confirming recognition of a person.
  • An embodiment assumes that in a detected event (e.g. as determined from the time information 1313), people tend to wear the same clothing.
  • a set of clothing variables CeJ may be introduced and used by a double-binding algorithm run on the CADI module 1340. Each such variable corresponds to the clothing of a particular person/ at event e.
  • FIG. 14B is another graphical representation of the Markov random field, with clothing incorporated into the model, under an embodiment of the invention.
  • the clothing descriptors can be obtained as follows:
  • ⁇ (pi, cj) ma ⁇ (exp(- ⁇ c ⁇ c(pi) - c(cj) ⁇ A 2), ⁇ c) where ⁇ c is the clothing importance weight, and ⁇ c is a clothing penalty threshold.
  • clothing can be also modeled not just for a particular event, but for the entire album as a whole.
  • clothing variables Cj can be connected to identity variables throughout the entire album. More complicated potentials may be necessary to capture the many possible items of clothing people possess. These potentials may be represented using mixture models, although other representations are also possible.
  • the remaining markers, such as sex and hair can also be incorporated in the algorithms performed by the CADI module 1340, in much the same or similar way as clothing recognition information 1344 is handled. Sex is clearly maintained through the entire album (with small exceptions). Hair appearance is normally preserved during a particular event, and is often preserved in the entire album.
  • the CADI module 1340 may capture this either by creating separate variables for hair and sex, similar to how clothing was used, the CADI module 1340 can create more complex variables which may capture a group of clothing/hair/sex descriptors simultaneously.
  • FIG. 15 illustrates a system for text recognition of text carried on objects in images, under an embodiment of the invention.
  • a system such as shown by FIG. 15 may correspond to text analysis component 1224 of the image analysis module 1220.
  • a system such as shown by FIG. 15 enables the analysis of image data for recognition of text carried on objects appearing in the image.
  • a system as shown also enables the use of recognized text for purposes of indexing and other uses.
  • a system includes text detector 1510, text processing component 1520, OCR 1530, and context and interpretation build 1540.
  • the text detector 1510 detects the presence of text on an object. For example, a scan of an image may be
  • 5 /8/2006 performed to detect edge characteristics formed by letters, as well as detection of other characteristics such as intensity, gradient direction, color information which correlate to the presence of text.
  • the text processing component 1520 may be used to normalize the appearance of the text image 1512. For example, the text processing component 1520 may normalize the appearance of text for skew, slope, scale factor and contrast yield, as described with other embodiments.
  • a processed text image 1522 is forwarded by the text processing component 1520 to the OCR.
  • the OCR recognizes the processed text image 1522, meaning that the text image is converted into text data 1532.
  • not all recognized text is material or relevant for use.
  • An understanding of the significance of the text is needed in order to, for example, have a need to index it.
  • the context and interpretation build component 1540 may perform programmatic steps in determining the significance of the recognized text data 1532.
  • the context and interpretation build component 1540 may employ a dictionary, thesaurus or other literary tool to determine the nature of the text data 1532. Another tool that is useful is a list of proper names of businesses, including companies with interstate commerce, and businesses of a local nature (a local restaurant).
  • context and interpretation build component 1540 may also receive and use metadata 1542 provided with the image file transferred.
  • This metadata may correspond to, for example, a file name of the image, a directory name from which the image file was copied, and an album name that carries the source image.
  • a file name of the image e.g. of a city
  • album name e.g. of a city
  • a recognition term 1544 may be outputted by the context and interpretation build component 1540, as a result of detection and interpretation of text on an object appearing in an image.
  • the recognition term 1544 may be indexed by the indexer 1560 so as to be associated with the image file in the index 1562.
  • the index 1562 may carry text information and correspond to, for example, ID Information Indexer 1240 of FIG. 12. This is
  • the recognition term 1544 may also be combined with the metadata 1566 carried in an image 1570, or be associated with the image as external metadata via a metadata store 1576.
  • FIG. 16 illustrates a system in which searching for images based on their contents can be performed, under an embodiment of the invention. According to one or more embodiments, the components shown by FIG. 16 may be integrated with other systems shown in FIG. 12 or elsewhere in this application.
  • a search and retrieval system is shown to include a user-interface 1710, an image analysis module 1720, and a search module 1730.
  • the image analysis module 1720 may be configured in a manner similarly described in other figures.
  • the search module 1730 corresponds to a component that matches search criteria with index values stored in one or more indexes. Specific indexes shown in FIG. 16 include a text index 1742 and a signature index 1744.
  • Embodiments contemplate different types of user-input, which are then converted into input for specifying a search criteria or criterion.
  • One type of input may correspond to an image file or image data 1702.
  • a person may submit a JPEG image of a face.
  • Another type of input may correspond to text input 1704.
  • the user may enter the proper name of an individual, assuming that person and his image are known to the system.
  • selection input 1708 another type of input that may be specified by the user is selection input 1708, which in one embodiment, may be based on the rendering of an objectified image 1706.
  • Objectified images 1706 are illustrated with FIG. 18 and FIG. 19, in that they present a digital image with recognized objects enabled as graphic user-interface features that are selectable.
  • the user-interface 1710 forwards input from the user to the search module 1730. If the input is image input 1702, the user-interface forwards image data input 1715 to the image analysis component 1720 as an intermediate step.
  • the image analysis component may recognize what, if any, objects in the image input 1702 are searchable.
  • suitable search criteria may correspond to (i) a face or portion of a person appearing in an image, (ii) text carried on an object, and (iii) any other recognizeable object, such as a landmark.
  • the operation of the image analysis component 1720 may be in accordance with
  • IiS O B ,/ :IL S 0 ,1 B any other module or method or technique relating to recognition of these types of objects in image.
  • search criteria may correlate to the color of clothing, or the color or type of hair, or similar looking faces.
  • the user-interface 1730 may forward the text input to the search module with little additional modification.
  • the user-interface 1710 may forward a signature and/or an identifier 1714 to the search module 1730.
  • the objectified image 1706 may carry identifiers, such as in the form of names or identities of individuals appearing in images, in the header of the objectified image 1706. Alternatively, as shown with FIG. 19, such metadata information and data may be stored in a separate data store, separate from the image file.
  • the user-interface 1710 may extract the identity of the person selected and forward that data as text to the search module 1730.
  • the selection input 1708 may correspond to submission of a recognition signature (or information) of the selected person/object.
  • the recognition signature may be used to determine similarity matching, even if the identity of the person is known.
  • the recognition signature may be a dimensional vector or value, and not a name or other text identifier.
  • the recognition signature is carried with the header of the image.
  • the recognition signature is determined by matching an identifier of the image to the recognition signature using a data store that is external or otherwise.
  • the search module 1730 may perform comparison functions of criteria to index data.
  • the input to the search module may be in the form of text data.
  • the search module receives text input as search criteria.
  • the search module 1730 uses a text criteria 1733 determined from the text input to determine image identifiers 1734 from the text index 1742. Then the search module 1730 may retrieve a search result 1738 comprising image files corresponding to the image identifiers 1734 from an image store 1746.
  • Signature input 1722 is not text based, and as such, the criteria 1732 derived from that input may be non-text, hi one embodiment, the criteria 1732 corresponds to the signature input 1722, and it is matched or compared (less precise than match) against other signatures in the signature index 1744. In one embodiment, a nearly exact match to the signature input 1722 is identified, meaning that the search result 1738 will comprise of images of the person who appears in the objectified image. In another embodiment, similarity matching is performed, meaning the search result 1738 may comprise of image files containing persons (or even dogs or animals) that are similar in appearance, but different than the person appearing in the image.
  • the components that comprise the search result may be progranimatically ordered in their presentation to the user. This may be accomplished using the following technique(s) and variations. As described in previous sections, images can be tagged for indexing and other purposes using various techniques. When a tag is searched, the system may invoke all the images with the particular search tag. In presenting, for example, a search result of all images with matching tags, an embodiment is provided that ranks the images in a programmatically determined order for purpose of presentation a user. In other words, this methodology answers the question of "which image comes first, and how are the results ordered".
  • the methodology uses a combination of metrics.
  • metrics can be confidence of the algorithm, consumption, difference measure, user picture ranking, and friend's images. These metrics are described as follows.
  • Confidence is usually an output metric that is useful in determining a presentation order for individual components of a search result.
  • the text recognition algorithm provides a confidence number regarding the text
  • a face recognition algorithm provides a confidence number regarding the faces.
  • Each of these confidence numbers can be used in deciding which result to show first. If the algorithm is more confident of its result, then those results are ranked higher, and shown first.
  • Consumption is defined as how much that image is viewed, and how often it is clicked to reach to other images and ads.
  • a programmatic element keeps a record of how many times each image is displayed and clicked.
  • the programmatic element is part of a service, and it maintained on a server. If an image is consumed and viewed more, then that image's rank is increased. [243] Difference measure is calculated using the visual signatures of the images. When the user does a particular search, the system makes sure that it does not show the same exact view and image of the search item or person.
  • the system includes a framework to rank each image as he or she views them. These user rankings are stored in the server's records. The user ranking can be used as part of the ranking process. The images that are ranked higher by the users are shown and served first.
  • a system can build a social network for everybody. For this, the system associates the people in one's photographs as his or her friends. In one embodiment, if a person does a search, and some of the hits to the search are actually images posted by his friends, then those images are ranked higher and served first.
  • search module 1730 may make a search request outside of the system shown in FIG. 16. For example, the search module 1730 may submit a search request based on the user-input to a third party network search engine (such as GOOGLE). In one embodiment, if the user input is text, then the request is the text submitted. If the user input is image, then text associated with the recognition of that image may be used. ,
  • a third party network search engine such as GOOGLE
  • an embodiment such as shown by FIG. 16 provides a system in which search may be performed with different kinds of user-inputs.
  • an embodiment shown by FIG. 16 enables search of images based on criteria that is in the form image data (e.g. a user-submitted image file), image data selection (e.g. user selects a selectable object from an objectified image rendering) or text input (e.g. user enters the name of a person).
  • image data e.g. a user-submitted image file
  • image data selection e.g. user selects a selectable object from an objectified image rendering
  • text input e.g. user enters the name of a person.
  • either kind of input can be used to search one of two indexes-text index 1742 or recognition index 1744.
  • recognition signature may be used to provide search results in response to image input.
  • recognition signatures of objects e.g. faces, people, text
  • Exact matching may be performed to find the same object (e.g. match a face with the same face in another image), or similarity matching may be performed to match an object with a look alike that is not the same object (e.g. show two people who look alike), while finding similar images/objects in a
  • recognition signatures may be calculated for objects recognized from images, or, if need be, for the entire image itself. Once a recognition signature is built, an n-level tree may be built to index all images. Such an index may correspond to, for example, recognition signature index 1252 of FIG. 12.
  • K-Means • cluster centers are saved at each node as the representation of that particular node. This way, for example, a billion images may be indexed in approximately 9 levels.
  • the tree link that has the closest representation match is chosen as the node, and a better match is searched in the children of that particular node. This process is repeated for every level of the tree until the algorithm reaches the leaves (a node that terminates) of the tree.
  • This is indeed a typical tree search algorithm, with recognition signatures as the indexes at the nodes.
  • an image can be compared against a billion in images with only ninety (91evels*10 branch/level) comparisons. As such, a fast image matching system can be built using this algorithm.
  • Embodiments of the invention provide for the use of objectified image renderings.
  • Objectified image renderings correspond to images that contain recognized objects, and these objects are interactive in some form with the user. For example, in one implementation, a user may hover a pointer over a rendering of an image on a computing device, and if the pointer is over an object that has previously been recognized, then information is displayed relating to or based on the recognition. In another implementation, a user may select an object that has previously been recognized from the image, and the selection becomes a criteria or
  • VM Page 58 specification for identifying and/or retrieving more images. Such an implementation is described with an embodiment of FIG. 16.
  • one embodiment provides for images to be displayed to a user in which individual images can be objectified so that recognized objects appearing in the image are capable of being interactive.
  • metadata of an image file may be supplemented with other data that identifies one or more recognized objects from the image file.
  • the supplemental data is used so that the one or more objects are each selectable to display additional information about the selected object.
  • FIG. 17 describes a method for creating objectified image renderings, under an embodiment of the invention. A method such as described may be implemented using various components and modules of different systems described with one or more embodiments of the invention.
  • step 1810 recognition information and data for a given image file is generated.
  • the recognition information may be in the form of metadata and text.
  • the metadata may identify what portion of the image of the file is recognized, such as for example, the region where a face in the image file is recognized.
  • the text portion of the recognition information may provide text-based recognition information, meaning that the recognition has been correlated to a name or other identifier of the recognition.
  • Step 1820 provides that the recognition information and data is associated with the image file. As shown in FIG. 18, one implementation provides that the recognition information and data is stored in a header of the image file. As shown in FIG. 19, another implementation may separate the recognition information and data from the image file.
  • the image file may be rendered in objectified form.
  • the user may open the image file from his personal computer and view the image.
  • the metadata makes active regions of the image that have recognition information associated with it. For example, a region of the image in which a face is provided may be made active, because recognition information (in the form of a name of the person) is associated with that region of the image.
  • the metadata makes the corresponding portion of the image active by identifying the location of the image that is to be made active.
  • the client application may be configured to make the image portions active based on reading the metadata. For example, the user may run an image viewer or browser that makes image portions active in response to interpreting the metadata in the header.
  • an action is detected in relation to the location of the image made active by the metadata.
  • This action may correspond to, for example, a selection action, such as in the form of a user clicking a mouse or pointer device.
  • the programmatic translation of the user performing the selection action may be one of design or implementation choice.
  • the programmatic action resulting from the user selection may be in one of the following: (i) displaying the text based recognition information associated with the region of the image, (ii) performing a search or retrieval of a library of images for images that are associated with the recognition information of the region in the image, (iii) submitting a search or retrieval to a network search engine (e.g. GOOGLE) based on the recognition information associated with the selected region of the image.
  • a network search engine e.g. GOOGLE
  • an objectified image file 1910 is represented as having one or more recognized regions 1912, 1914, and 1916. Consistent with various embodiments described, the recognized regions 1912-1916 may correspond to persons (including faces), text carried on objects, and other designated objects such as landmarks.
  • the metadata tags and indexes
  • the metadata can be saved in various forms and locations.
  • the metadata is saved as part of the image header data. As an example, but without any limitation, it can be saved as part of the EXIF data.
  • metadata stored in the header of an image file can be encoded. Coding in the image header enables the image data from the image file to be read independent of platform or location.
  • the metadata is written to the image header, yet it is not encoded in any ways. In this case, the image and the metadata can be editable, and extendable by any programs and by anybody. This provides a chance for the metadata to be universal.
  • the image file 1910 includes a header 1920 in which (i) object metadata 1930 and (ii) recognition information 1940 is provided.
  • the header 1920 may also include metadata normally provided with an image file, such as image identifier 1918, and creation or modification time.
  • the object metadata 1930 indicates regions in the image where recognized objects are provided, such as the coordinates defining the regions where the person 1912, the text 1914 or other object 1916 are provided, as well as their corresponding recognition confidence values. While showing an image using a viewer, first the metadata is loaded from the header of the image, or from the central server. The metadata is then displayed as part of
  • the recognition information 1940 is correlated, meaning it is text that is correlated to a recognition signature or other quantitative indication of the object recognized.
  • the recognition information may be a name, for the text
  • the recognition information may be an identifier of what that object is.
  • FIG. 20 shows an example of an image in which the metadata can be displayed in an interactive manner, so as to make the image an objectified image rendering. Once the mouse is on the image, all the tagged faces, text and possible other examples are shown as an overlay on top of the image.
  • recognition information 1940 may correspond to extrapolated information.
  • the recognition information for the text 1914 may be words or content associated with the recognized word.
  • object metadata 1930 may be associated with additional data or information that is relevant to one of the recognized objects when the image file is rendered.
  • the recognition information 1940 associated with the person 1912 may further be supplemented with a biography of the person.
  • the biography of the person may appear when the user selects the person's face.
  • the biography data may be carried in the header, or the header may include a link or pointer to it.
  • the text may have associated with it a URL to a particular web site.
  • Various combinations and alternatives are contemplated consistent with these examples.
  • FIG. 19 illustrates another embodiment in which metadata 1930 and recognition information 1940 is stored in a data store 1970, external to the image file being rendered.
  • a client application may match the image file (e.g. by image file identifier in the header file) with the object metadata 1930 (defining position of recognized object) and recognition information 1940 (providing recognition of defined positions).
  • the location of the data store 1970 may be anywhere.
  • the data store 1970 may be located on a network when the image is rendered on a client, or located on the terminal of the client. image with its metadata. This scheme assures that metadata is kept securely, and it is shared based on permissions.
  • a visual signature is calculated for every image, and saved as part of the metadata at a central server.
  • a visual signature is calculated and compared against the visual signatures. If a visual signature matches, then the metadata associated with it is assigned to the image.
  • Visual signatures may be maintained in an index such as described with FIG. 12 and with FIG. 16, but for visual signature and recognition signatures may be different in what they represent.
  • recognition signatures may be for objects in images, while visual signatures are more for identifiers of the whole image.
  • the visual signature of an image is calculated by getting the color or grey scale histogram of the image.
  • the histogram is invariant to rotation and scale of the image.
  • a thumbnail of the image is used as its visual signature.
  • the image is uniformly divided into several smaller rectangle regions.
  • a histogram is calculated for each rectangular region, and the collection of the histograms is used as the visual signature.
  • a hash value of the image is used as a visual signature/ID for the image. Identification of images that match the visual signature of an image may be provided using a fast search algorithm described elsewhere, where the visual signature of the image is used as a comparison against other visual signatures.
  • Similarity matching means that an image of a person may be quantitatively recognized, then compared to find another person deemed to be similar to the person recognized from the image.
  • the result (whether by image or otherwise) is of a person who is different than the person recognized.
  • similarity matching may be performed as a search and retrieval operation, where the search criteria is a face (e.g. the user's face), and the search result is a look-alike to that person.
  • search criteria is a face (e.g. the user's face)
  • the search result is a look-alike to that person.
  • Specific examples include a person submitting his picture to find someone else who looks like him, or a person submitting his picture to find a person who he resembles that is famous.
  • FIG. 20 illustrates a basic system for enabling similarity matching of people, under an embodiment of the invention.
  • image data 2010 including a person (or portion thereof) is received by an analysis module 2020.
  • the analysis module 2020 may recognize the person in the image, through any of the techniques described with embodiments of the
  • the analysis module 2020 may correspond to the image analysis module 1220 of FIG. 12.
  • a system of FIG. 20 includes a database 2030 containing the recognition signatures of a library of people.
  • a system of FIG. 20 is implemented as a network service, such as provided over the Internet.
  • the database 2030 may include recognition signatures 2032 from numerous users of the system, or alternatively, from non- users who have images available for recognition determination.
  • the database 2030 may include recognition signatures 2032 from celebrities or other people that are famous or well known.
  • the analysis module 2020 may perform a comparison operation on the contents of the database for the recognition signature 2032 that most closely match or are similar to the signature of the image most recently analyzed. Similarity matches 2034 may be returned to the person, in the form of images of persons deemed to be similar in appearance, as determined by a comparative standard set by the system. As an alternative to returning images, the identity or name of the similar looking person may be returned. [275] One result of an embodiment such as shown is that a person can enter his picture to discover his nearest known look-alike (the "lost twin").
  • an embodiment provides that the user can enter as input an image of a person to be recognized, and specify (or provide as input to be recognized or otherwise) the individual that the recognized image is to be compared against. For example, a user may enter his own picture and specify the celebrity he or she wishes to be compared against. Or the user may enter his picture, and the picture of a family member, and request a programmatic comparison that states how close the two family members are in appearance. In either case, the result provided in such an embodiment may be
  • the face is the primary source of features for performing both recognition and determining similarity matching. Given a face, the system can extract features from the face that describe the given face. These features are then used to find similar faces. Similar faces will have closely matching features. [278] The more faces that exist in the database, the better the similarity search results will be. However, the features that describe the faces lie in a high-dimensional space and finding the most similar faces from a large high-dimensional dataset is extremely computationally expensive.
  • the performance of the matching is computationally intensive, particularly when the database being matched against has a large number of signatures.
  • a tree- structure as described below to search the high-dimensional space efficiently.
  • the feature vector of a face includes information derived from principal component analysis (PCA). PCA is applied to several regions of the detected face.
  • the face feature vector may include the union of the PCA of all the face regions, which include the whole face, the left eye, and the right eye.
  • the face feature vector may include color histogram information.
  • color histograms may be computed for the hair region and the skin region of a person being recognized.
  • the face may be detected automatically in a manner such as described with FIG. 3. Once detected, the face position in the image can be used to determine a skin box and hair box in the image.
  • the color histograms are computed for the skin and hair boxes.
  • the feature vector includes information on the sex of the face, the ethnicity, and the hairstyle. This information can come from both automatic classification and from user provided data. Machine learning may be used to train classifiers to determine sex, ethnicity, and hairstyle from user data and the detected faces.
  • the similarity of two faces is computed by comparing the two corresponding feature vector.
  • the similarity score is the sum of the absolute value difference of each term in the feature vector (the Ll distance norm). In another embodiment, an L2 norm distance can be used.
  • one embodiment may also enable search for similar faces in closed and/or related sets.
  • closed set include the user's own set, a set consisting of only the user's friends, or friends of friends datasets.
  • Such an embodiment may have entertainment value, as well as enable a means by which individuals can be introduced to one another, such as through a social networking service.
  • embodiments such as provided above detail similarity matching as between people, other embodiments may match a person to a dog or other animal.
  • training and/or classification may be used to better correlate certain animal features, such as eye position, shape and color, to comparative features of people.
  • the face is the primary source of features for performing both recognition and determining similarity matching. Given a face, the system can extract features from the face that describe the given face. These features are then used to find similar faces. Similar faces will have closely matching features. [286] The more faces that exist in the database, the better the similarity search results will be. However, the features that describe the faces lie in a high-dimensional space and finding the most similar faces from a large high-dimensional dataset is extremely computationally expensive.
  • a search algorithm may a tree-structure such as described below to search the high- dimensional space efficiently.
  • Such an embodiment may utilize a feature vector.
  • the feature vector includes of a face includes information derived from principal component analysis (PCA). PCA is applied to several regions of the detected face.
  • the face feature vector includes the union of the PCA of all the face regions, which include the whole face, the left eye, and the right eye.
  • the face feature vector may include color histogram information. Specifically, color histograms may be computed for the hair region and the skin region of a person being recognized. The face is detected automatically, and the face position
  • C TZ-US Q B ⁇ JL aOJlG in the image can be used to determine a skin box and hair box in the image.
  • the color histograms are computed for the skin and hair boxes.
  • the feature vector includes information on the sex of the face, the ethnicity, and the hairstyle. This information can come from both automatic classification and from user provided data. Machine learning may be used to train classifiers to determine sex, ethnicity, and hairstyle from user data and the detected faces.
  • the different parts of the feature vector are weighted by their importance and combined into a single face feature vector.
  • the particular weighting used may be one of design implementation.
  • the similarity of two faces is computed by comparing the two corresponding feature vector.
  • the similarity score is the sum of the absolute value difference of each term in the feature vector (the Ll distance norm). In another embodiment, an L2 norm distance can be used.
  • one embodiment may also enable search for similar faces in only the user's own data set, and only in the user's friends, or friends of friends datasets.
  • Such an embodiment may have entertainment value, as well as enable a means by which individuals can be introduced to one another, such as through a social networking service.
  • embodiments such as provided above detail similarity matching as between people, other embodiments may match a person to a dog or other animal.
  • training and/or classification may be used to better correlate certain animal features, such as eye position, shape and color, to comparative features of people.
  • the image features used for similarity matching are image coloring.
  • color histograms may be determined for the whole image and/or regions of the image. Images with the same color are more likely to be similar. Also, by comparing color histograms of regions, images with similar shape/structure are favored.
  • the color can be combined with texture information. Gabor filters is an example of a method for which texture features of objects appearing in an image may be determined.
  • a shape features appearing in the given image may also be used as well. As an example, but without any limitation, the shape features can be
  • the edges of the image are found first, and statistical characteristics of the edges are used as the shape features.
  • the similarity score of two images is the weighted sum of the image feature match (color histograms) and the text tag match.
  • the image feature is a vector (generated from the color histograms) and the Ll distance norm is used to compute the image match score.
  • the text tag match is the number of matching tags weighted by their confidence. In another embodiment, only either of the image feature match or text tag match are used.
  • a tree data structure may be utilized in a recognition signature index (e.g. recognition signature index 1252 of FIG. 12) as a basis for comparing recognition signature input to other signatures.
  • a tree structure enables efficient search of large high-dimensional datasets. Partitioning a high-dimensional space with a tree will split some similar feature vectors so that they are far apart in the search tree even though they are near each other in the high- dimensional space. (A split here means a partition of the high dimensional space by a hyperplane.) To keep similar feature vectors together, the search algorithm uses multiple trees with different splitting points.
  • the different split points in each tree are computed randomly with their probability determined by how split points partition the data. Having multiple trees with different split points will keep similar feature vectors close together in some of the trees, and lower the probability that similar feature vectors are missed entirely because they are far away in all the trees. The union of the search results from all the trees will yield a good set of similar results. This will keep the cost of searching the large dataset low and the number of missed similar results low.
  • the tree is stored as a hashtable.
  • the split points are used to compute a hash value that maps each feature vector to the corresponding hash bucket/leaf node.
  • This hash function stores the hierarchical structure of the tree, so that the data/feature vectors can be stored in a flat hashtable.
  • the hash function can be generated in several ways.
  • the hash function can be generated completely randomly.
  • Locality Sensitive Hashtables LSH
  • LSH Locality Sensitive Hashtables
  • the other extreme is to greedily pick the hash function.
  • the drawback of this approach is that it does not work for multiple hash functions.
  • the two approaches can be combined to generate multiple hash functions. Hash functions are sampled randomly while weighted in a greedy way.
  • the system allows users to select a region in the image and search for images having regions similar to the selected region.
  • the system returns several results for this type of search. The first is the most matching text tags. Next is the image features and the text tags as in the previous section. And last is the image features without text tags.
  • the system allows similarity searches on automatically detected text in the images. This does a text search of the top synonyms and associated words. This is an "or” search on the synonyms.
  • Photographs can be used to build a social network of people that know each other.
  • a social network may be programmatically built in-part through image recognition and some of the techniques described with various other embodiments.
  • a service may be provided that scans images from members or other users.
  • the service may operate under various assumptions that aid the social network development. One assumption is that two people know each other if they have a picture together.
  • a server maintains the images and tags collected from images in which recognition processes are performed. Using the face information in the images, the server can construct a social network for everybody registered with the service.
  • the social network may exist in the form of data that interconnects two or more persons as associates (e.g. friends or acquaintance).
  • Social interconnections amongst people may have a range of degrees of separation.
  • a social networking service may manage such data, so as to know how persons are interconnected by one or more degrees of separation.
  • the server stores visual signatures for all the names/email addresses trained by users.
  • 5/8/2Q06 information from the server More specifically, one embodiment provides that a service downloads visual signatures for all or some of the email addresses in the user's address book. Then, these visual signatures are compared against the visual signatures of the faces found by the system. In one embodiment, a nearest neighbor classifier can be used for this comparison. In another embodiment, the weighted nearest neighbor is used for this comparison.
  • the faces with visual signatures that are very close to the visual signatures of the address book entries are assigned to each other. In other words, the system would know those particular faces without any user input.
  • the training sets can be shared along with shared photographs. For instance, if a Person A shares his photos of Person B with Person B, the system automatically gets the face signatures (training set) of Person B. In addition the system can share other related people's face signatures (training sets). For instance, the system can share the face signatures of Person A, and also the face signatures of people that co-occur frequently with Person B (in Person A's photo set). These shared training sets can be used for recognition in Person B's photo set. This way, many people are automatically recognized in Person B's photo set without any work from Person B. [305] In another embodiment, the images of a person can be obtained automatically via other websites.
  • the person can be registered at a personal date site, or a social networking site.
  • This websites usually carry the person's photograph, as well as friend's photographs in it.
  • the system asks the user his access information to these web sites, such as login and password. Then, the system can go to these web sites, and automatically import the pictures, and add to the training set. This way, some photographs of the person are automatically recognized.
  • tags can be extracted from images, using recognition information and signatures, as well as metadata about the image.
  • a service e.g. server or host
  • an inverse index may be created such that, for given a tag, what is provided are all the images that contain that tag.
  • the PicRank algorithm determines the most relevant images with that tag.
  • Any given text content may be subjected to inclusion of an image file.
  • the text content may correspond to, for example, a text article or an email.
  • the article may be inspected for purpose of determining what images may be relevant to it. For example, in one implementation, key words may be determined by counting reoccurring words and analyzing words in the title or subject line of an article. These words might be filtered by a proper noun dictionary if necessary.
  • the central server is connected and a search is applied on the index or tags of a library of images, using words of the article deemed to be most relevant. The most relevant search image results are returned, and they are automatically posted next to the images.
  • search results may be returned in an order of relevancy, using an algorithm that detects such relevancy.
  • FIG. 22 illustrates an implementation of an embodiment described.
  • the image matched to the article is commercial in nature, in that it shows an example of a device that is the subject of the article. Selection of the image may cause a link selection, so that the user's web browser is directed to a web site where the product in the image is sold, or where more information about the image or the underlying product is provided.
  • an overlay (such as shown in FIG. 20 and related embodiments) on the images can be shown when the mouse is on the images.
  • the page might be directed to the web page of the actual product item, or full search page of the item from the central server. This way, photos are included to add value to the article, as well as, ads are displayed in images, and in a non-disturbing manner to the user.

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Abstract

Dans un mode de réalisation, l'invention permet de récupérer une collection d'images capturées afin de former au moins une partie d'une bibliothèque d'images. Pour chaque image de la collection, on peut analyser une image capturée afin de reconnaître les informations à partir des données image contenues dans l'image capturée et on peut générer un index, l'index de données étant basé sur les informations reconnues. L'index permet d'activer une fonctionnalité telle que la recherche et la récupération. Diverses techniques de reconnaissance, y compris celles qui font appel au visage, aux vêtements, à l'habillement et à des combinaisons de caractéristiques, peuvent être utilisées. La reconnaissance peut s'effectuer sur des personnes et sur un texte porté par des objets, entre autres.
PCT/US2006/018016 2005-05-09 2006-05-09 Systeme et procede permettant l'utilisation d'images capturees par reconnaissance WO2006122164A2 (fr)

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US67959105P 2005-05-09 2005-05-09
US60/679,591 2005-05-09
US11/246,434 2005-10-07
US11/246,742 2005-10-07
US11/246,741 2005-10-07
US11/246,742 US7519200B2 (en) 2005-05-09 2005-10-07 System and method for enabling the use of captured images through recognition
US11/246,589 US7809192B2 (en) 2005-05-09 2005-10-07 System and method for recognizing objects from images and identifying relevancy amongst images and information
US11/246,589 2005-10-07
US11/246,434 US7783135B2 (en) 2005-05-09 2005-10-07 System and method for providing objectified image renderings using recognition information from images
US11/246,741 US7809722B2 (en) 2005-05-09 2005-10-07 System and method for enabling search and retrieval from image files based on recognized information

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Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009031923A1 (fr) * 2007-08-10 2009-03-12 Obschestvo S Ogranichennoy Otvetsvennostyu, 'rekogmission' Procédé d'indexation d'objets contenus dans des images numériques et procédé de recherche d'objets dans des images numériques
WO2010065064A1 (fr) * 2008-11-25 2010-06-10 Eastman Kodak Company Segmentation de pilosité
WO2013036495A1 (fr) * 2011-09-07 2013-03-14 Eastman Kodak Company Procédé de classification d'événements utilisant une détection de sources de lumière
US8659764B2 (en) 2009-02-27 2014-02-25 Body Surface Translations, Inc. Estimating physical parameters using three dimensional representations
US20140082023A1 (en) * 2012-09-14 2014-03-20 Empire Technology Development Llc Associating an identity to a creator of a set of visual files
CN108292311A (zh) * 2015-11-11 2018-07-17 三星电子株式会社 用于处理元数据的设备和方法
CN110502711A (zh) * 2019-07-17 2019-11-26 汉海信息技术(上海)有限公司 页面展示方法、装置、电子设备及可读存储介质
CN110520737A (zh) * 2017-04-13 2019-11-29 美国西门子医学诊断股份有限公司 用于在试样表征期间进行标签补偿的方法和装置
CN112955911A (zh) * 2018-10-08 2021-06-11 谷歌有限责任公司 数字图像分类和注释
US20210407016A1 (en) * 2018-09-20 2021-12-30 The Toronto-Dominion Bank Dynamic provisioning of data exchanges based on detected relationships within processed image data
US11875683B1 (en) * 2019-10-02 2024-01-16 Samsara Inc. Facial recognition technology for improving motor carrier regulatory compliance

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5781650A (en) * 1994-02-18 1998-07-14 University Of Central Florida Automatic feature detection and age classification of human faces in digital images
US6819783B2 (en) * 1996-09-04 2004-11-16 Centerframe, Llc Obtaining person-specific images in a public venue
US6035055A (en) * 1997-11-03 2000-03-07 Hewlett-Packard Company Digital image management system in a distributed data access network system
FR2806814B1 (fr) * 2000-03-22 2006-02-03 Oce Ind Sa Procede de reconnaissance et d'indexation de documents
US7130466B2 (en) * 2000-12-21 2006-10-31 Cobion Ag System and method for compiling images from a database and comparing the compiled images with known images
KR100355382B1 (ko) * 2001-01-20 2002-10-12 삼성전자 주식회사 영상 시퀀스에서의 객체 레이블 영상 생성장치 및 그 방법
JP2005115672A (ja) * 2003-10-08 2005-04-28 Fuji Photo Film Co Ltd 画像処理装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
None

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009031923A1 (fr) * 2007-08-10 2009-03-12 Obschestvo S Ogranichennoy Otvetsvennostyu, 'rekogmission' Procédé d'indexation d'objets contenus dans des images numériques et procédé de recherche d'objets dans des images numériques
WO2010065064A1 (fr) * 2008-11-25 2010-06-10 Eastman Kodak Company Segmentation de pilosité
US8270682B2 (en) 2008-11-25 2012-09-18 Eastman Kodak Company Hair segmentation
US8659764B2 (en) 2009-02-27 2014-02-25 Body Surface Translations, Inc. Estimating physical parameters using three dimensional representations
WO2013036495A1 (fr) * 2011-09-07 2013-03-14 Eastman Kodak Company Procédé de classification d'événements utilisant une détection de sources de lumière
US8634661B2 (en) 2011-09-07 2014-01-21 Intellectual Ventures Fund 83 Llc Event classification method using light source detection
US20140082023A1 (en) * 2012-09-14 2014-03-20 Empire Technology Development Llc Associating an identity to a creator of a set of visual files
CN108292311A (zh) * 2015-11-11 2018-07-17 三星电子株式会社 用于处理元数据的设备和方法
CN108292311B (zh) * 2015-11-11 2022-11-15 三星电子株式会社 用于处理元数据的设备和方法
CN110520737A (zh) * 2017-04-13 2019-11-29 美国西门子医学诊断股份有限公司 用于在试样表征期间进行标签补偿的方法和装置
CN110520737B (zh) * 2017-04-13 2023-09-05 美国西门子医学诊断股份有限公司 用于在试样表征期间进行标签补偿的方法和装置
US20210407016A1 (en) * 2018-09-20 2021-12-30 The Toronto-Dominion Bank Dynamic provisioning of data exchanges based on detected relationships within processed image data
CN112955911A (zh) * 2018-10-08 2021-06-11 谷歌有限责任公司 数字图像分类和注释
CN110502711A (zh) * 2019-07-17 2019-11-26 汉海信息技术(上海)有限公司 页面展示方法、装置、电子设备及可读存储介质
CN110502711B (zh) * 2019-07-17 2022-08-26 汉海信息技术(上海)有限公司 页面展示方法、装置、电子设备及可读存储介质
US11875683B1 (en) * 2019-10-02 2024-01-16 Samsara Inc. Facial recognition technology for improving motor carrier regulatory compliance
US12002364B1 (en) 2019-10-02 2024-06-04 Samsara Inc. Facial recognition technology for improving driver safety

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