PATENT ABSTRACT
An image recognition information attaching apparatus includes a retrieving unit that retrieves image information on a per piece basis of identification information, from the image information having the identification information associated thereto in advance, a generator unit that generates feature information from the image information retrieved by the retrieving unit, and a learning unit that provides a learning result by learning a relation between the feature information generated by the generator unit and the identification information of the image information corresponding to the feature information, using a stochastic model including a mixture of a plurality of probability distributions.

PATENT DESCRIPTION
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is based on and claims priority under 35 USC 119 from Japanese Patent Application No. 2011-267118 filed Dec. 6, 2011. 
     BACKGROUND 
     (i) Technical Field 
     The present invention relates to an image recognition information attaching apparatus, an image recognition information attaching method, and a non-transitory computer readable medium. 
     (ii) Related Art 
     One of related art image recognition information attaching apparatuses learns a relation between feature information and identification information (hereinafter referred to as a “label”) in advance if the identification information that is attached in accordance with the feature information resulting from image information or the like is prepared in advance. In accordance with the learning results, the image recognition information attaching apparatus recognizes the label to which input image information belongs. 
     SUMMARY 
     According to an aspect of the invention, an image recognition information attaching apparatus is provided. The image recognition information attaching apparatus includes a retrieving unit that retrieves image information on a per piece basis of identification information, from the image information having the identification information associated thereto in advance, a generator unit that generates feature information from the image information retrieved by the retrieving unit, and a learning unit that provides a learning result by learning a relation between the feature information generated by the generator unit and the identification information of the image information corresponding to the feature information, using a stochastic model including a mixture of a plurality of probability distributions, the learning unit calculating, from a first variable determined from the feature information belonging to one of the probability distributions, and a variable describing a probability distribution determined from a set of the feature information resulting from all the image information retrieved by the retrieving unit regardless of the content of the identification information, a second variable in accordance with a contribution ratio responsive to a density of the feature information belonging to the one of probability distributions, and learning the relation using a distribution described by the second variable. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Exemplary embodiments of the present invention will be described in detail based on the following figures, wherein: 
         FIG. 1  illustrates an example of an image recognition information attaching apparatus of one exemplary embodiment of the present invention; 
         FIGS. 2A and 2B  illustrate an example of a basic learning operation; 
         FIG. 3A  illustrates a relationship of a k-th Gaussian distribution of label c, overall image feature distribution, and mean value μ k   C  determined by a model learning unit, and  FIG. 3B  diagrammatically illustrates distributions of feature vectors and ranges of data regions; 
         FIG. 4  is a flowchart illustrating an operation of the image recognition information attaching apparatus; 
         FIG. 5  is a flowchart illustrating a learning algorithm; and 
         FIG. 6  is a flowchart illustrating an operation of the image recognition information attaching apparatus. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  illustrates a configuration of an image recognition information attaching apparatus  1  of an exemplary embodiment of the present invention. 
     The image recognition information attaching apparatus  1  includes controller  10 , storage  11 , and communication unit  12 . The controller  10  controls elements including a central processing unit (CPU), and executes a variety of programs. The storage  11  includes storage units such as a hard disk drive and flash memory. The communication unit  12  communicates with the outside via a network. 
     An image input via the communication unit  12  may include as objects a “river,” a “mountain,” a “child,” and the like. Words such as a “river,” a “mountain,” and a “child” are hereinafter referred to as annotation words. The image recognition information attaching apparatus  1  attaches to the image information the annotation word as identification information (hereinafter referred to as a “label”). The image recognition information attaching apparatus  1  performs a learning process using learning image information with a label attached thereto in advance and stored on the storage  11  or the like. 
     The controller  10  executes an image recognition information attaching program  110  to be discussed, and thus functions as image retrieving unit  100 , image partitioning unit  101 , feature vector generator unit  102 , learning data set retrieving unit  103 , overall image feature distribution estimating unit  104 , model learning unit  105 , likelihood calculating unit  106 , annotation word estimating unit  107 , and output unit  108 . 
     In a learning process, the image retrieving unit  100  selects and retrieves image information for learning from image information  111  stored on the storage  11 . In estimating the label, the image retrieving unit  100  retrieves image information input from an external terminal apparatus via the communication unit  12 . 
     The image partitioning unit  101  partitions the image information retrieved by the image retrieving unit  100  and the image information  111  for learning stored on the storage  11  into multiple regions, thereby generating partial segments. The image partitioning unit  101  may use a method of partitioning the image information in accordance with rectangles arranged in a mesh, or a method of defining near and similar pixels as belonging to the same segment in accordance with a clustering technique, such as k-nearest neighbor algorithm. 
     The feature vector generator unit  102  generates a feature vector from each of the partial segments generated by the image partitioning unit  101 , using a method of Gabor filter, or a method of extracting feature quantity such as RGB, normalized RG, CIELAB, or the like. The feature vector is one example of the feature information. 
     The learning data set retrieving unit  103  retrieves from the image information  111  image information that the same label is imparted to, and retrieves as a learning data set a set of feature vectors included in the retrieved image information. The learning data set retrieving unit  103  also retrieves a feature vector set (hereinafter referred to as a “universal model”) resulting from all the image information  111  regardless of the content of the label. The selection of the learning data set is not limited to a method of retrieving all the learning data. For example, if an amount of learning data is extremely large, another method may be used. For example, in one method, data elements are randomly extracted from all the learning data until a specified number of data elements are obtained. 
     The overall image feature distribution estimating unit  104  learns the universal model as a prior probability model, and estimates learning results (hereinafter referred to as an “overall image feature distribution”). 
     The model learning unit  105  learns the learning data set retrieved by the learning data set retrieving unit  103 , and includes a data density estimating unit  105   a  and a parameter optimization unit  105   b.    
     The data density estimating unit  105   a  estimates a data density of data in a data region of a given label. The “data region” herein refers to a region in a space of the feature vectors belonging to a k-th Gaussian distribution if the entire space of the feature vectors is segmented into K Gaussian distributions in accordance with Gaussian mixture model (GMM) (see  FIG. 3A ). More information is provided in detailed in learning process described below. The “data density” refers to a density of data included in the data region of the k-th Gaussian distribution. 
     The parameter optimization unit  105   b  calculates and optimizes a second variable from a first variable determined from the feature information belonging to the data region, and a variable describing the overall feature distribution, in accordance with a contribution ratio. The contribution ratio is determined by the data density of the data region estimated by the data density estimating unit  105   a.    
     The likelihood calculating unit  106  calculates the likelihood of any label of the feature vector of the image information retrieved by the image retrieving unit  100 . 
     The annotation word estimating unit  107  estimates an annotation word corresponding to the label having a high likelihood, as the identification information of the input image information. 
     The output unit  108  outputs, to a display unit, a printer, the storage  11 , or the like, several annotation words having high likelihood, from among those estimated by the annotation word estimating unit  107 . In this way, the output unit  108  presents an annotation word to be output according to the likelihood. The user of the image recognition information attaching apparatus  1  may select an appropriate annotation word from the presented annotation words according to the likelihood. 
     The storage  11  stores image recognition information attaching program  110 , image information  111 , label information  112 , learning information  113 , and the like. The image recognition information attaching program  110  causes the controller  10  to operate as the elements of the controller  10 . The image information  111  is used in the learning process. The label information  112  associates the image information included in the storage  11  with the label. The learning information  113  is the learning result of the model learning unit  105 . 
     Referring to the drawings, the operations of the image recognition information attaching apparatus  1  are described in terms of a basic learning operation, a detailed learning operation, and an annotation estimation operation. 
       FIG. 4  is a flowchart illustrating the operation of the image recognition information attaching apparatus  1 . 
       FIGS. 2A and 2B  generally illustrate the basic learning operation. 
     The image retrieving unit  100  receives the image information  111  as the learning data from the storage  11  (S 1 ). For example, the image information  111  includes multiple pieces of image information associated with annotation words a “mountain,” a “sun,” a “car,” and the like as labels. 
     The image partitioning unit  101  partitions a display image of image information  111   a  illustrated in  FIG. 2A  as one example of the image information retrieved by the image retrieving unit  100  into n segments of  FIG. 2B . The image partitioning unit  101  thus results in partial segments A 1 -A n  (S 2 ). In one example, the display image is partitioned into rectangles arranged in a mesh. That operation may be performed on each of the multiple pieces of image information retrieved by the image retrieving unit  100 . 
     The feature vector generator unit  102  extracts multiple feature quantities f 1 -f D  from the partial segments A 1 -A n , for example, using the Gabor filter. The feature vector generator unit  102  thus generates feature vectors x 1 , x 2 , . . . , x n  of the partial segments A 1 -A n , each having the feature quantities f 1 -f D  as the components thereof (S 3 ). That operation may be performed on each of the multiple pieces of image information retrieved by the image retrieving unit  100 . 
     The learning data set retrieving unit  103  references the label information  112 , and retrieves the image information associated with a label c 1  (for example, the annotation word “mountain”) from the image information  111 . The learning data set retrieving unit  103  retrieves a set of feature vectors generated from the retrieved image information as a learning data set (S 4  and S 5 ). 
     The model learning unit  105  learns the learning data of the label c 1  retrieved by the learning data set retrieving unit  103  (S 6 ), and stores the learning result in the learning information  113  on the storage  11  (S 7 ). 
     Operations in steps S 5  through S 7  are performed on all the labels (M labels) (S 8  and S 9 ). 
     The detailed learning operation performed by the model learning unit  105  in step S 6  is described in detail below. 
     The model learning unit  105  uses GMM as a probability generation model. Let X={x 1 , . . . , x n } represent an input learning data set, and D represent the dimension of the feature vector, and Gaussian mixture model p is defined by expression (1) as follows: 
                     p   ⁡     (     X   |   c     )       =         ∏     i   =   1     N     ⁢     p   ⁡     (       x   i     |   c     )         =       ∏     i   =   1     N     ⁢       ∑     k   =   1     K     ⁢       π   k   c     ⁢     N   ⁡     (         x   i     |     μ   k   c       ,     Σ   k   c       )                       (   1   )               
where N is the number of input learning data elements, and K is the number of mixture elements. Let π k   c  represent a mixture ratio, N(x i |μ k   c , Σ k   c ) represent a D-dimensional Gaussian distribution having mean value μ k   c and variance Σ k   c .
 
     The mixture ratio satisfies expression (2): 
     
       
         
           
             
               
                 
                   
                     
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     The overall image feature distribution estimating unit  104  learns as a prior probability common to all the labels a model (universal model) where all the image information  111  is set as the learning data set. The model is referred to as an overall image feature distribution in the present invention. According to the exemplary embodiment, the overall image feature distribution is represented by the following GMM: 
     
       
         
           
             
               
                 
                   
                     
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     The mixture ratio π k   c , the mean value μ k   c  and the variance Σ k   c  (1≦k≦K) are obtained by performing a learning process in advance through a standard expectation-maximization (EM) algorithm. The learning process is performed using a learning data set of all the labels set in a learning data setting process (or learning data set randomly extracted with no label defined). 
     The parameter optimization unit  105   b  performs a first method to correct the Gaussian distribution N(x i |μ k   c , Σ k   c ) corresponding to a given label using the overall image feature distribution. When the parameter optimization unit  105   b  calculates parameters (the mixture ratio, the mean value, and the variance) using the EM algorithm in the first method, the initial values of the parameters are those of the overall image feature distribution. 
     The EM algorithm has a feature of dependence on the initial value. The smaller the number of data elements is, the larger the dependence on the initial value becomes. If the reliability of the learning data is low with a small number of learning data samples, the Gaussian distribution reflecting the overall image feature distribution may be obtained. If the number of learning data samples is large, the Gaussian distribution reflecting the trend of the learning data samples more may be obtained. 
     The model learning unit  105  uses a second method to correct the Gaussian distribution N(x i |μ k   c , Σ k   c ) corresponding to a given label using the overall image feature distribution. In the second method, the model learning unit  105  uses the overall image feature distribution as a prior distribution. With a specific GMM used as a prior distribution, and the parameters of the Gaussian distribution (second variables) are calculated as follows: 
                     π   k   c     =           ∑     i   =   1       N   c       ⁢     r   ik   c       +   τ         N   c     +     τ   ⁢           ⁢   K                 (   4   )                 μ   k   c     =           ∑     i   =   1       N   c       ⁢       r   ik   c     ⁢     x   i         +     τμ   k   u             ∑     i   =   1       N   c       ⁢     r   ik   c       +   τ               (   5   )                 Σ   k   c     =             ∑     i   =   1       N   c       ⁢       r   ik   c     ⁢     x   i     ⁢     x   i   T         +     τ   ⁢     {       Σ   k   u     +         μ   k   u     ⁡     (     μ   k   u     )       T       }               ∑     i   =   1       N   c       ⁢     r   ik   c       +   τ       -         μ   k   c     ⁡     (     μ   k   c     )       T               (   6   )               
where r ik   c , called shared ratio, is a posterior distribution of mixture elements k if data x i  is given, and is defined by the following expression (7):
 
                     γ   ik   c     ≡         π   k   c     ⁢     N   ⁡     (         x   i     |     μ   k   c       ,     Σ   k   c       )             ∑     k   =   1     K     ⁢       π   k   c     ⁢     N   ⁡     (         x   i     |     μ   k   c       ,     Σ   k   c       )                     (   7   )               
where τ is a real constant number, and N c  is the number of learning data elements of label c.
 
     From expressions (4) through (6), it is understood that the smaller the amount of learning data is, the more the parameters (second variables) of the Gaussian distribution reflects the parameters of the overall image feature distribution. 
     Expression (5) may be interpreted as follows: 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                             
                         
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     Expression (8), if represented in diagram, is illustrated in  FIGS. 3A and 3B . 
       FIG. 3A  illustrates a relationship of a k-th Gaussian distribution of label c, overall image feature distribution, and mean value μ k   c  determined by the model learning unit  105 . For simplicity of explanation, the feature vector is one-dimensional, and each small blank circle represents a data sample. 
     The data density estimating unit  105   a  estimates a data density N k   c  in accordance with expression (8-2). Here τ is a predetermined constant, and as the data density N k   c  is smaller, the model learning unit  105  results in, as a calculation result of the mean value μ k   c  (second variable), closer to mean value μ k   u  of the overall image feature distribution. As the data density N k   c  is larger, the model learning unit  105  results in, as a calculation result of the mean value μ k   c  (second variable), closer to sample mean  x   k   c  (first variable) of a region k of label c. 
     Similarly, 
                           ⁢         Σ   k   c     =       ρ   ⁢           ⁢       x   _     k     c   ⁢           ⁢   2         +       (     1   -   ρ     )     ⁢     {       Σ   k   u     +         μ   k   u     ⁡     (     μ   k     u   ⁢               )       T       }       -         μ   k   c     ⁡     (     μ   k   c     )       T         ⁢     
     ⁢         x   _     k     c   ⁢           ⁢   2       ≡           ∑     i   =   1       N   c       ⁢       r   ik   c     ⁢     x   i     ⁢     x   i   T             ∑     i   =   1       N   c       ⁢     r   ik   c         ⁢     :     ⁢           ⁢   Root   ⁢           ⁢   mean   ⁢           ⁢   square   ⁢           ⁢   of   ⁢           ⁢   the   ⁢           ⁢   samples   ⁢           ⁢   in   ⁢           ⁢   region   ⁢           ⁢   k   ⁢           ⁢   of   ⁢           ⁢   label   ⁢           ⁢   c                 (   9   )               
where π k   c  defines a data density of the region k of the label c as follows:
 
π k   c ∝Σ i=1   N     c     r   ik   c +τ  (10)
 
     If expression (10) is normalized using expression (2), expression (4) results. 
     In the model learning unit  105 , the data density estimating unit  105   a  estimates the data density of the data region, and the parameter optimization unit  105   b  determines in response to the data density a contribution ratio that reflects the parameter of the overall image feature distribution. 
     If τ is given, each label c is learned using the EM algorithm. 
     The learning algorithm using the EM algorithm is described in detail below. 
       FIG. 5  is a flowchart illustrating the learning algorithm.  FIG. 3B  diagrammatically illustrates distributions of feature vectors and ranges of data regions. For simplicity of explanation, the feature vector is two-dimensional, and each small blank circle represents a data sample. 
     The parameter optimization unit  105   b  in the model learning unit  105  initializes the parameters {π k   c , μ k   c , Σ k   c } (S 11 ). The parameter optimization unit  105   b  determines the initial value of the parameter of the overall image feature distribution using the universal model. 
     In the results of step S 11 , the data sample belongs to any of the data region of the Gaussian distribution. The model learning unit  105  calculates the shared ratio r jk  of the data sample belonging to each Gaussian distribution in E step in accordance with expression (7). 
     The model learning unit  105  then updates the parameters {π k   c , μ k   c , Σ k   c } in M step in accordance with expressions (4) through (6) (S 13 ). In the results of step S 13 , the data sample belongs to any of the data regions of the Gaussian distributions governed by the update parameters. 
     The model learning unit  105  determines whether a convergence condition is satisfied or not (S 14 ). If a change in logarithmic likelihood is equal to or lower than a predetermined value (yes from S 14 ), the model learning unit  105  completes the calculation step thereof. If the change in the logarithmic likelihood is higher than the predetermined value (no from S 14 ), the model learning unit  105  returns to step S 12 . 
     The model learning unit  105  stores learned parameters {π k   c , μ k   c , Σ k   c } of the model of each label on the storage  11  as the learning information  113 . 
       FIG. 6  is a flowchart illustrating the annotation estimation operation. 
     The image retrieving unit  100  retrieves via the communication unit  12  image information input from the outside as a label estimation target (S 21 ). 
     The image partitioning unit  101  partitions the image into n segments, thereby generating the partial segments (S 22 ). 
     The feature vector generator unit  102  extracts multiple feature quantities from each of the partial segments, and generates respectively for the partial segments the feature vectors x 1 , x 2 , . . . , x n  having these feature quantities as the components thereof (S 23 ). 
     The likelihood calculating unit  106  reads from the learning information  113  the model of each label learned in step S 6  (S 24 ). More specifically, the likelihood calculating unit  106  reads from the storage  11  the parameters {π k   c , μ k   c , Σ k   c } of the model and then expands the parameters {π k   c , μ k   c , Σ k   c } onto a memory (not illustrated). 
     The likelihood calculating unit  106  calculates the posterior probability of the feature vector of each partial segment (S 25 ). When the set X={x 1 , . . . , x n } of the feature vectors extracted from an input image I to be predicted is provided, the likelihood calculating unit  106  calculates the posterior probability p(c|X) of the label c using Baye&#39;s theorem as follows: 
                     p   ⁡     (     c   |   X     )       =       p   ⁡     (     c   |       x   1     ⁢           ⁢   …   ⁢           ⁢     x   n         )       =         p   ⁡     (   c   )         p   ⁡     (       x   1     ⁢           ⁢   …   ⁢           ⁢     x   n       )         ⁢       ∏     i   =   1     n     ⁢     p   ⁡     (       x   i     |   c     )                     (   11   )               
where p(c) is the posterior probability of the label c, and relative frequency in the learning data set is used for p(c). p(x 1  . . . x n ) is the posterior distribution of the feature vector set, and takes a constant value with respect to label. The logarithmic likelihood of the label c of the image I is expressed with the constant portion thereof removed as follows:
 
     
       
         
           
             
               
                 
                   
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     The larger the magnitude of expression (12) is, the better the label is for the image I. Several results of expression (12) in the order of the large to the small magnitude are used as labels for the image I (annotation words). 
     The likelihood calculating unit  106  calculates the likelihood of the feature vector x i  of a partial image of a given label c (S 26 ). 
     When the likelihood is calculated, the annotation word estimating unit  107  retrieves five labels, for example, in the order of the large to the small magnitude, and attaches annotation words to the labels as the identification information of the image information (S 27 ). 
     The output unit  108  outputs annotation word estimation results to a predetermined output device (not illustrated) such as a display, a printer, or a hard disk (S 28 ). 
     The present invention is not limited to the above-described exemplary embodiment, and may be changed into a variety of modifications within the scope of the present invention. 
     The image recognition information attaching program  110  used in the exemplary embodiment may be read onto the storage  11  within the image recognition information attaching apparatus  1  from a recording medium such as compact disk read-only memory (CD-ROM), or may be downloaded onto the storage  11  within the image recognition information attaching apparatus  1  from a server or the like connected to a network such as the Internet. The storage  11  may be arranged external to the image recognition information attaching apparatus  1 . The external storage  11  and the image recognition information attaching apparatus  1  may be connected to via the network. Part or whole of the image retrieving unit  100  through the output unit  108  may be implemented using a hardware structure such as an application specific integrated circuit (ASIC). 
     The foregoing description of the exemplary embodiments of the present invention has been provided for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations will be apparent to practitioners skilled in the art. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, thereby enabling others skilled in the art to understand the invention for various embodiments and with the various modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the following claims and their equivalents.