Source: https://patents.google.com/patent/US10275714B2/en
Timestamp: 2019-12-08 19:26:10
Document Index: 640960991

Matched Legal Cases: ['Application No. 201180008344', 'Application No. 201180008344', 'Application No. 11740453', 'Application No. 11740453', 'Application No. 11740453', 'Application No. 201180008344']

US10275714B2 - Image tagging based upon cross domain context - Google Patents
Image tagging based upon cross domain context Download PDF
US10275714B2
US10275714B2 US14/151,773 US201414151773A US10275714B2 US 10275714 B2 US10275714 B2 US 10275714B2 US 201414151773 A US201414151773 A US 201414151773A US 10275714 B2 US10275714 B2 US 10275714B2
US14/151,773
US20140129489A1 (en
Simon John Baker
2010-02-04 Priority to US12/699,889 priority Critical patent/US8645287B2/en
2014-01-09 Priority to US14/151,773 priority patent/US10275714B2/en
2014-05-08 Publication of US20140129489A1 publication Critical patent/US20140129489A1/en
2019-04-30 Publication of US10275714B2 publication Critical patent/US10275714B2/en
This application is a continuation of U.S. patent application Ser. No. 12/699,889, filed on Feb. 4, 2010, and entitled “IMAGE TAGGING BASED UPON CROSS DOMAIN CONTEXT”, the entirety of which is incorporated herein by reference.
An interface component 110 may access the data store 102 and retrieve a subset of the images 104 for presentation to a user 112. Pursuant to an example, the interface component 110 can cause a graphical user interface to be generated and displayed to the user 112 on a display screen of the computing device, wherein the graphical user interface facilitates receiving labels from the user for an image or series of images. The interface component 110 may receive labels assigned to images by the user 112, and may cause such labels to be stored in correspondence with the appropriate images. For example, the user may assign a) a first label to an image to indicate an identity of one or more persons in the image; b) a second label to an image to indicate an event that corresponds to the image; and/or c) a third label to an image to indicate a location that corresponds to the image. The interface component 110 may be configured to instruct the user as to how many labels to assign with respect to one or more images.
These labels assigned by the user 112 to a subset of the images 104 can be used in the system to automatically learn relationships between domains and to automatically infer labels to assign to images. As used herein, a “domain” can refer to a particular type of label: for instance, a first type of label may be identities of people, a second type of label may be events that correspond to images, and a third type of label may be locations corresponding to images. Thus, the interface component 110 can act in an interactive manner, requesting that the user 112 assign labels to certain images and providing the user 112 with guesses as to labels of images that the user 112 can confirm. This interactivity can cause the automated labeling of images to occur in a more user-friendly and efficient manner.
k ⁡ ( x 1 , x 2 ) = exp ⁡ ( - 1 σ 2 ⁢ d 2 ⁡ ( x 1 , x 2 ) ) .
Here d(x1, x2) and k(x1, x2) are respectively the distance in kernel value between the features x1 and x2. The kernel parameter σ can be set for different types of features respectively through cross-validation. If a feature extracted by the extractor component 114 is a time stamp, the kernel value can be set to one when time stamps are within a same time unit and zero otherwise. The time unit can be set based upon the application. For instance, the time unit may be a day, an hour, a week, etc.
Now more detail will be provided with respect to generating a probabilistic framework that includes models of domains as well as relational models. It can be assumed that there are M domains, and the Yth domain can be denoted by Yu. The number of elements and distinct labels in Yu can be denoted by Nu and Ku, respectively. The label of each element in Yu can be modeled by a random variable yu:i that can take values in {1, . . . , Ku}, where i is the index of the element Yu. The feature corresponding to the element yu:i can be denoted by xu:i. Furthermore, it can be assumed that two distinct elements (in different domains or the same domain) co-occur if they are associated with a same image. The co-occurrence of the element in the domain Yu and Yv can be captured by the indicator function couv, and covu, as defined by the following algorithm:
co uv ⁡ ( i , j ) = co vu ⁡ ( j , i ) = { 1 y u : i ⁢ ⁢ and ⁢ ⁢ y v : j ⁢ cooccur 0 otherwise ( 1 )
Ruv can denote a relational model (e.g., the relational model 124) between the domains Yu and Yv (e.g., the domains modeled by the models 120 and 122), which can be parameterized by a matrix of size Ku×Kv. The entry Ruv(k,l) can be the coupling coefficient between class k of domain Yu and class l of domain Yv. A larger value of a coupling coefficient can indicate higher probability that the elements of corresponding classes are associated with a same image.
log p(Y * |X * ;R *)=Σu=1 MαuΦu(Y u |X u)+
αuvΦuv(Y u ,Y v |R uv−log Z(X * ;R *)) (2)
Here, Y*, X*, and R* respectively denote the labels, features and relational models while Yu, Xu, denotes labels and features of domain Yu. αu and αuv, are positive weights that can control the contribution of different terms in Equation (2).
The term Φu can be referred to as the affinity potential, which can capture the feature-based affinities between elements in domain Yu. The affinity potential can be defined as follows:
Φu(Y u |X u)=Σi=1 N u Σj=1 N u w u(i,j)
(y u:i =y u:j)=Σi=1 N u Σj=1 N v w u(i,j)Σk=1 K u δk(y u:i)δk(y u:j). (3)
Here, wu(i,j) is the feature based affinity between yu,i and yu,j, which can be set to the similarity value between the corresponding features xu,i and xu,j.
(⋅) is an indicator where
(yu:i=yu:j) equals 1 when the labels yu,i and yu,j are substantially similar, and equals 0 otherwise. δk is also an indicator, which can be defined by δk(y)=
(y=k). The affinity potential Φu can reflect the rationale that elements with similar features are likely to be in the same class.
The term Φuv can be referred to as the relation potential, which can capture contextual relations between the domains Yu and YYv. The relational potential can be given by the following algorithm:
Φuv(Y u ,Y v |R uv)=Σi=1 N u Σj=1 N v co uv(i,j)Φuv(y u:i ,y v:j), (4)
where the co-occurrence potential Φuv can be given by the following algorithm:
Φuv(y u:i ,y v:j)=Σk=1 K u Σl=1 K v R uv(k,l)δk(y u:i)δl(y v:j). (5)
From Equation (4) it can be discerned that the relation potential is the sum of the potential of all co-occurring pairs between Yu and Yv. High co-occurrence potential ϕuv(Yu:i, Yv:j) can be obtained when elements are assigned a pair of labels with high coupling coefficients. Hence Φuv can be understood as the compliance of the label assignment with the relational model 117. While the models 120 and 122 and the relational model 124 have been described in the context of tagging images, it is to be understood that utilization of a relational model to improve output of probabilistic models can be applied to other contexts.
Φ(Y P |X C)=Σi=1 NΣj=1 N w C(i,j)
(y i =y j)
(e i =e j) (6)
In this example, YP and XC respectively denote labels for people and clothes features, wC(i,j) is a similarity between the clothes of the ith and jth persons. ei and ej are the labels of events associated with the images capturing the two persons. The major difference between Equation (6) and Equation (3) is the incorporation of the factor
(ei=ej) in the Equation (6). This factor can serve as a switch that turns the features off when they are compared across events, for example.
p ⁡ ( R ) = 1 z prior ⁢ exp ⁡ ( - β 1 ⁢  R  1 - β 1 ⁢  ℛ  2 2 ) , ( 7 )
where ∥R∥1 and ∥R∥2 are respectively the L1 and L2 norms of the coupling coefficient matrix.
J o(R)=log p(Y L |X;R)+log p(R)=log ΣY u p(Y L ,Y U |X;R)+X;R)+log p(R) (8)
Here, YL and YU can denote the labels of the elements labeled by the user and unlabeled elements, respectively. The inference component 126 can infer distribution of YU based at least in part upon the substantially optimized R. Both the learning and inference stages of the system 100 (the learning component 128 and the inference component 126) can utilize marginalization over YL on a loopy probabilistic network, which can be computationally intractable.
This computationally intractability can be addressed by adopting a variational approach. For instance, instead of directly maximizing Jo(R), the learning component 128 can maximize a variable lower bound as follows:
J(R,q)=ΣY U q(Y U)log p(Y L ,Y U |X;R)−ΣY U q(Y U)log q(Y U)−log Z(X;R)+log p(R), (9)
where q is a distribution of YU. Furthermore, the following can be shown:
J(R,q)≤J 0(R),∀q, (10)
and the equality holds when q(YU) equals the posterior of YU (e.g., p(YU,YL|X;R)). Thus, by substantially maximizing the variational objective J(R,q), the learning component 128 can learn the relational model 124 and the inference component 126 can infer the posterior distribution of the labels substantially simultaneously. In an example, this can be learned via the coordinate ascent strategy, which can iteratively update R and q by the following:
q ^ ( t + 1 ) = argmax q ⁢ ⁢ J ⁡ ( R ^ ( t ) , q ) ( 11 ) R ^ ( t + 1 ) = argmax R ⁢ ⁢ J ⁡ ( R , q ^ ( t + 1 ) ) ( 12 )
Such two formulas respectively correspond to label inference (as undertaken by the inference component 126) and relational learning as undertaken by the learning component 128. As both formulas optimize the same objective, convergence can be guaranteed.
q(Y U)=Πu=1 M q u(Y u:U)=Πu=1 MΠi∈U u q u:i(y u:i ), (13)
where Yu:U and Uu denote label variables and indices of all unlabeled elements, respectively. With such factorized approximation, qu:i can be iteratively updated for each element with other elements fixed. The updating formula can be derived as follows:
q ^ u : i ⁡ ( k ) = exp ⁡ ( ψ u : i ⁡ ( k ) ) ∑ k ′ = 1 K u ⁢ ⁢ exp ⁡ ( ψ u : i ⁡ ( k ′ ) ) , ( 14 )
where ψu:i(k) can be understood as the weight of assigning label k to the ith element in Yu and qu:i(k) can be the normalized exponential of ψu:i(k). The value of ψu:i(k) can be as follows:
ψu:i(k)=αuψu:i (u)(k)+Σv=1 Mαuvψu:i (v→u)(k), (15)
where ψu:i (u) can be given by the following:
ψu:i (u)(k)=Σj=1 N u w u(i,j)q u:j(k) (16)
This term captures the information from the elements with similar features and combines the label distribution of other elements in the same domain and weights their contributions according to feature affinities.
For conditional features as described above, the weight wu(i,j) can be modified to be wu(i,j)Eq{
(ei=ej)}. In other words, the affinity weights can be modulated by the probability that such affinity weights are associated with a substantially similar event. If there are multiple features used to characterize the elements in the domain, the affinity weights can be added together.
The term ψu:i (v→u) (k) can represent messages from domain Yv to Yu which can be given by the following:
ψu:i (v→u)(k)=Σj=1 N v co uv(i,j)Σl=1 K v R(k,l)q v:j(l) (17)
This formulation can be described as follows: The formulation first singles out co-occurring elements in domain Yv (by couv(i,j)) and then retrieves their labels (denoted by l). The weight of assigning an element to class k can be determined by its coupling coefficient with the co-occurring label (given by R(k,l)). The formula is simply a probabilistic version of such a reasoning procedure.
Additional details pertaining to learning the relational model 124 are now provided. According to Equation (12), given the inferred posterior q, the learning component 128 can learn the relational model 124 by maximizing the following objective function:
E q{log p(Y L ,Y U |X;R)}−log Z(X;R)+log p(R) (18)
In solving this problem, a difficulty is faced in that computation of the log partition function log Z(X;R) is intractable. Since the probabilistic formulation is in exponential family, an upper bound of the log partition function log Z(X;R) can be derived using tree reweighted approximation. That is, the original model can be divided into a convex combination of simpler submodels, and the corresponding convex combination of the log partition functions of these submodels constitute an upper bound of the original log partition function.
Σu=1 Mθu A u=
θuv B uv(R uv/θuv) (19)
Here, Au is the log partition function corresponding to the within-domain affinity graph of domain YU, which does not depend on relational models. Buv is the log partition function corresponding to the cross-domain relational graph between domains Yu and Yv, which is a function of Ruv. θu and θuv are convex combination coefficients which satisfy Σu=1 Mθu+
θuv=1. In an example θu can be set to zero and
θ uv = 1 #
B uv(R)=Σi=1 N u Σj=1 N v co uv(i,j)Σk=1 K u Σl=1 K v exp(R uv(k,l)) (20)
The complexity of Buv(R) is O(mu,Ku,Kv), where mu is the number of co-occurring pairs between Yu and Yv.
Substituting the upper bound of log Z(X;R) given in Equation (19) for log Z(X;R) results in a variational learning problem that is to maximize a concave lower bound of the original objective. In this problem the learning of each relational model is separated. In particular, Ruv can be solved by maximizing the following objective:
E q{Φuv(Y u ,Y v |R uv)}−θuv B(R/θ uv)+log p(R uv), (21)
E q{Φuv(Y u ,Y v |R uv)}=Σi=1 N u Σj=1 N v co uv(i,j)Σk=1 K u Σl=1 K v R(k,l)q u:i(k)q v:j(l) (22)
In an example, L-BFGS algorithm can be used to solve this problem. Since the objective function is concave, global maxima is warranted.
As used herein, the terms “component” and “system” are intended to encompass hardware, software, or a combination of hardware and software. Thus, for example, a system or component may be a process, a process executing on a processor, or a processor. Further, a system or component may be a series of transistors or a portion of computer memory. Additionally, a component or system may be localized on a single device or distributed across several devices.
causing a graphical user interface to be displayed on a display screen of a computing device, the graphical user interface configured to receive assignations of labels to respective images in an image collection;
receiving input from a user of the computing device by way of the graphical user interface, the input being an assignment of a first label to a first image in the image collection, the first label identifying an event captured in the first image;
updating a relational model based upon the first label assigned to the first image, the relational model models relationships between people in images of the image collection and events represented in the images of the image collection;
subsequent to updating the relational model, providing, without user input, a second image to a probabilistic model that is configured to assign labels to images in the image collection, wherein the labels identify people captured in the images in the image collection, and further wherein the probabilistic model assigns a second label to the second image based upon features of the second image and output of the relational model,
the second label identifies a person in the second image; and
updating the relational model based upon the second label assigned to the second image by the probabilistic model, such that the relational model is updated as the probabilistic model assigns the labels to images in the image collection and the probabilistic model assigns the labels based upon the relational model.
subsequent to assigning the second label to the second image, requesting feedback from the user as to the second label being assigned to the second image.
3. The method of claim 2, the feedback being confirmation from the user that the second label properly identifies the person in the second image.
assigning, without user input, a third label to a third image in the image collection based upon the first label assigned to the first image and the second label assigned to the second image, the third label identifying a location at which the third image was captured.
5. The method of claim 1, wherein the output of the relational model is a probability that the person is captured in the second image.
receiving second input from the user by way of the graphical user interface, the second input being an assignment of a third label to the first image in the image collection, the third label identifying a location at which the first image was captured; and
assigning the second label to the second image based upon the third label assigned to the first image.
outputting, by way of the graphical user interface, a plurality of guesses as to labels to assign to respective images of the image collection, the labels corresponding to the guesses being events possibly captured in the respective images;
receiving feedback from the user that confirms at least one guess in the plurality of guesses; and
assigning the second label to the second image based upon the plurality of guesses and the feedback.
8. The method of claim 7, further comprising updating the relational model based upon the plurality of guesses and the feedback.
outputting, by way of the graphical user interface, a plurality of guesses as to labels to assign to respective images of the image collection, the labels corresponding to the guesses identifying people possibly captured in the respective images;
extracting a feature from the first image, the feature being indicative of the event captured in the first image, the feature having a value;
updating the relational model based upon the value of the feature; and
assigning the second label to the second image based upon the updating of the relational model.
causing a graphical user interface to be displayed on a display screen of a computing device, the graphical user interface configured to receive, from a user of the computing device, assignations of respective labels to images in an image collection;
receiving, by way of the graphical user interface and from the user, an assignation of a first label for a first image in the image collection, the first label identifying an event captured in the first image;
updating a relational model based upon the first label assigned to the first image, wherein the relational model models relationships between people captured in the images in the image collection and events captured in the images in the image collection;
subsequent to updating the relational model, providing a second image in the image collection to a probabilistic model that is configured to assign labels to images in the image collection, wherein the labels identify people captured in the images in the image collection, and further wherein the probabilistic model assigns a second label to the second image based upon features of the second image and output of the relational model; and
without user input, updating the relational model based upon the second label assigned to the second image by the probabilistic model, wherein the probabilistic model assigns the labels to the images in the image collection based upon outputs of the relational model, and further wherein the relational model is updated based upon the labels assigned to the images in the image collection by the probabilistic model.
12. The system of claim 11, the acts further comprising:
receiving feedback from the user that confirms that the second label is to be assigned to the second image; and
updating the relational model responsive to receipt of the feedback.
13. The system of claim 11, the acts further comprising:
updating at least one relationship in the relationships modeled by the relational model responsive to assigning the second label to the second image.
14. The system of claim 11, the acts further comprising:
updating a second relational model responsive to assigning the second label to the second image, the second relational model configured to model relationships between the events captured in the images of the image collection and locations at which respective images in the image collection were captured.
15. The system of claim 11, the acts further comprising:
extracting at least one feature having at least one value from the first image, the at least one feature and the at least one value being indicative of the event, wherein the probabilistic model assigns the second label for to the second image is inferred based upon the at least one feature and the at least one value.
16. The system of claim 11, the acts further comprising:
receiving an assignation of a third label to the first image, the third label identifying a second person captured in the first image; and
assigning the second label to the second image based upon the third label being assigned to the first image.
17. The system of claim 11, the acts further comprising:
receiving an assignation of a third label to the first image, the third label identifying a location at which the first image was taken, and wherein the second label is assigned to the second image based upon the third label being assigned to the first image.
18. The system of claim 11, wherein the first label is a name that is descriptive of the event.
receiving, by way of the graphical user interface, a user assignment of a first label to a first image in the image collection, the first label being a name of an event that is captured in the first image;
responsive to receiving the user assignment of the first label to the first image, updating a relational model based upon the user assignment of the first label to the first image, the relational model models relationships between events represented in the images in the image collection and people captured in the images in the image collection;
subsequent to updating the relational model, assigning, through use of a probabilistic model, people labels to the images in the image collection, wherein the people labels identify people in the images in the image collection, the probabilistic model assigns the people labels based upon outputs of the relational model, and further wherein the relational model is updated based upon the people labels assigned to the images in the image collection by the probabilistic model.
20. The computer-readable data storage device of claim 19, wherein the relational model is further updated based upon event labels assigned to the images in the image collection by a second probabilistic model, the event labels identify the events represented in the images of the image collection.
US14/151,773 2010-02-04 2014-01-09 Image tagging based upon cross domain context Active US10275714B2 (en)
US12/699,889 US8645287B2 (en) 2010-02-04 2010-02-04 Image tagging based upon cross domain context
US14/151,773 US10275714B2 (en) 2010-02-04 2014-01-09 Image tagging based upon cross domain context
US12/699,889 Continuation US8645287B2 (en) 2010-02-04 2010-02-04 Image tagging based upon cross domain context
US16/374,551 Continuation US20190362247A1 (en) 2019-04-03 Image tagging based upon cross domain context
US20140129489A1 US20140129489A1 (en) 2014-05-08
US10275714B2 true US10275714B2 (en) 2019-04-30
ID=44342491
US12/699,889 Active 2031-09-27 US8645287B2 (en) 2010-02-04 2010-02-04 Image tagging based upon cross domain context
US14/151,773 Active US10275714B2 (en) 2010-02-04 2014-01-09 Image tagging based upon cross domain context
US (2) US8645287B2 (en)
EP (1) EP2531913A4 (en)
CN (1) CN102741815B (en)
HK (1) HK1174992A1 (en)
WO (1) WO2011097517A2 (en)
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2011-02-04 WO PCT/US2011/023795 patent/WO2011097517A2/en active Application Filing
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