Patent Abstract:
A pattern recognition method comprises steps of inputting a pattern of a recognition object performing feature extraction from the input pattern to generate a feature vector, increasing the number of quantization in an order from quantization number 1 or quantization number 2 to calculate a quantization threshold of each of the quantization number, wherein the quantization threshold of quantization number (n+1) using a quantization threshold of quantization number n (n&gt;=1) is calculated and a quantization function having a quantization threshold corresponding to quantization number S (S&gt;n) is generated, quantizing each component of the feature vector of the input pattern using the quantization function to generate an input quantization feature vector having each of the quantized component, storing a dictionary feature vector of the recognition object, or a quantized dictionary feature vector in which each component of the dictionary feature vector of the pattern of a recognition object is quantized; calculating a similarity between the input quantization feature vector and the dictionary feature vector, or a similarity between the input quantization feature vector and the quantized dictionary feature vector; and recognizing the recognition object based on the similarity.

Full Description:
CROSS REFERENCE TO RELATED APPLICATION 
       [0001]    This application is based upon and claims the benefit of priority from the Japanese Patent Application No. 2008-060990, filed on Mar. 11, 2008, the entire contents of which are incorporated herein by reference. 
       FIELD OF THE INVENTION 
       [0002]    The invention relates to a pattern recognition apparatus which reduces memory of feature value, and a method thereof, suppressing degradation of recognition performance. 
       DESCRIPTION OF THE BACKGROUND 
       [0003]    In pattern recognition, “Pattern classification, Richard O. Duda, Peter, E. Hart, David G. Stork, Wiley-Interscience” has disclosed a method of reducing memory of a feature vector, suppressing degradation of recognition performance. 
         [0004]    Application of this reduction method provides a subspace where sum of the square error of the feature vector approximation by projection is at a minimum. Projection on the subspace allows us to reduce dimension of the feature vector and memory, keeping the square error of the whole feature vector small. Unlike a data compression, distance and angle between the feature vectors can be calculated in an approximate state without returning to the original condition. 
         [0005]    However, the above mentioned reduction method poses a problem that the amount of memory of the feature vectors may not be sharply reduced, suppressing degradation of recognition performance, since the dimension of the subspace to be projected needs to be remained to some extent in order to maintain recognition performance. 
       SUMMARY OF THE INVENTION 
       [0006]    The invention allows compression of feature vectors to reduce amount of memory, suppressing degradation of recognition performance, without returning to the original state. 
         [0007]    An embodiment of the invention provides a pattern recognition apparatus which comprises a pattern input unit configured to input a pattern of a recognition object, a feature extraction unit configured to perform feature extraction from the input pattern to generate a feature vector, a function generation unit configured to increase the number of quantization in an order from quantization number 1 or quantization number 2 to calculate a quantization threshold of each of the quantization number, the function generation unit calculating the quantization threshold of quantization number (n+1) using a quantization threshold of quantization number n (n&gt;=1) and generating a quantization function having a quantization threshold corresponding to quantization number S (S&gt;n), a quantization unit configured to quantize each component of the feature vector of the input pattern using the quantization function to generate an input quantization feature vector having each of the quantized component, a dictionary unit configured to store a dictionary feature vector of the recognition object, or a quantized dictionary feature vector in which each component of the dictionary feature vector of the pattern of a recognition object is quantized, a similarity calculation unit configured to calculate a similarity between the input quantization feature vector and the dictionary feature vector, or a similarity between the input quantization feature vector and the quantized dictionary feature vector, and a determination unit configured to recognize the recognition object based on the similarity. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]      FIG. 1  is a block diagram of a pattern recognition apparatus according to the first embodiment of the invention. 
           [0009]      FIG. 2  is a flow chart of quantization processing. 
           [0010]      FIG. 3  is a flow chart of a similarity calculation processing. 
           [0011]      FIG. 4  is a block diagram of a pattern recognition apparatus according to the second embodiment of the invention. 
           [0012]      FIG. 5  is a flow chart of a similarity calculation processing. 
           [0013]      FIG. 6  is a block diagram of a pattern recognition apparatus of a third embodiment. 
           [0014]      FIG. 7  is a view of a quantization processing of each component of a feature vector generated from a face image. 
           [0015]      FIG. 8  is a view of a vector (v 1 , v 6 ) rearranged when D=6. 
           [0016]      FIG. 9  is a view of e i,j  of the vector of  FIG. 8 . 
           [0017]      FIG. 10  is a view of vectors E i,M  and T i,M  of  FIG. 8 . 
           [0018]      FIG. 11  is a view of the quantization threshold search processing  204  for the vector of  FIG. 8  in a case where N=3. 
           [0019]      FIG. 12  is a view of re-search preparation processing  206  for the vector of  FIG. 8  in a case where N=3 and i=5. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0020]    Reference will now be made in detail to the present embodiments of the invention, embodiments of which are illustrated in the accompanying drawings. A pattern recognition apparatus of the embodiments of the invention will be explained with reference to the drawings as follows. The pattern recognition apparatus of the embodiments is not limited to pattern recognition of an image but may be applied to various fields of pattern recognitions where a feature value such as a sound is used. 
       First Embodiment 
       [0021]    Pattern recognition apparatus  10  of this embodiment quantizes both an input feature vector corresponding to an input pattern and a dictionary feature vector which is a candidate for comparison, calculates a similarity, and performs pattern recognition based on the similarity. 
         [0022]    Pattern recognition apparatus  10  of this embodiment will be explained with reference to  FIG. 1 .  FIG. 1  is a schematic block diagram illustrating pattern recognition apparatus  10 . Pattern recognition apparatus  10  comprises a pattern input unit  101 , a feature extraction unit  102 , a feature vector quantization unit  103 , a dictionary feature storing unit  104 , a similarity calculation unit  105 , and a determination unit  106 . Functions of each unit  101 - 106  may be realized by a program stored in a computer. Functions of each unit  101 - 106  will be explained below. 
         [0023]    Pattern input unit  101  inputs the pattern to be used as a candidate for recognition. When the pattern is an image, an image captured by, for example, a digital camera may be input into a computer or an image captured by a camera connected to a computer may be input into the computer. When the pattern is a sound, for example, a recorded sound may be input into a computer or a sound recorded by a microphone connected to a computer may be input into the computer. 
         [0024]    Feature extraction unit  102  extracts feature values from a pattern input by pattern input unit  101  and converts the pattern into a vector. Hereafter, the vector converted by feature extraction unit  102  is called a “feature vector.” When the input pattern is an image, the pattern is changed into a vector by, for example, a raster scan. When the input pattern is a sound, for example, a vector which has frequency components of the sound within a definite period of time is used. 
         [0025]    After converting into a vector, a processing of suppressing an input pattern change may be performed. For example, a processing of removing a noise component with a small eigen value obtained from the pattern prepared in large quantities beforehand by principal component analysis etc. may be performed. 
         [0026]    Feature vector quantization unit  103  generates a quantization function for the feature vector (the input feature vector and the dictionary feature vector) generated by feature extraction unit  102 , and performs quantization processing of each component of the feature vectors based on the quantization function. The “quantization function” is a function defined from a set of sections which is divided from a set of real numbers into limited numbers or countable infinite numbers, and a set of values corresponding to the sections one-to-one, and the function which outputs the value corresponding to the section containing the input real number for the input real number. The “quantization threshold” means the above section. The “quantization value” is the value corresponding to the quantization threshold one-to-one and is included in the quantization threshold. The “quantization feature vector” means a feature vector quantized by feature vector quantization unit  103 . 
         [0027]      FIG. 2  is a flow chart of details of quantization processing  20  performed in feature vector quantization unit  103 . Explanation for quantization processing  20  will be explained below. The image processed by quantization processing  20  for the feature vector generated from the face image is shown in  FIG. 7 . 
         [0028]    Dictionary feature storing unit  104  extracts a dictionary feature vector for a pattern of each class for recognition by feature extraction unit  102 , performs processing by feature vector quantization unit  103  and stores quantization feature vectors (hereinafter, referred to as “dictionary quantization feature vector”) of the generated dictionary into a storage area. Similarity calculation unit  105  calculates a value indicating a similarity (hereinafter, referred to as “similarity”) between a quantization feature vector of the input pattern generated by feature vector quantization unit  103  (hereinafter, referred to as “input quantization feature vector”) and a dictionary quantization feature vector of each class stored in dictionary feature storing unit  104 . Here, the distance between vectors is calculated. 
         [0029]      FIG. 3  is a flow chart of a similarity calculation processing  30  performed by similarity calculation unit  105 . Explanation of similarity calculation processing  30  is mentioned later. Determination unit  106  identifies the class for recognition, when the class has the highest similarity among the registered classes fulfilling conditions of the similarity. When no class fulfills the conditions, determination unit  106  identifies that there is no class in the class. When the distance between vectors is used as the similarity, the distance is set to be smaller than a predetermined threshold and has higher similarity as the distance becomes smaller. 
         [0030]    Quantization processing  20  is a quantization processing performed by feature vector quantization unit  103 .  FIG. 2  is a flow chart of quantization processing  20 . Quantization processing  20  includes a feature vector input processing  201 , a rearrangement processing  202 , an initialization processing  203 , a quantization threshold search processing  204 , an error/quantization quantification processing  205 , a re-search preparation processing  206 , and a quantization feature vector output processing  207 . Explanation of each of the processing  201 - 207  is shown below. 
         [0031]    Feature vector input processing  201  is a processing of inputting the feature vector (i.e., the input feature vector or the dictionary feature vector) output from feature extraction unit  102 . Hereafter, the dimension of the feature space of the feature vector is set to D. 
         [0032]    Rearrangement processing  202  is a processing of rearranging the size of the value of each component of the feature vectors into an ascending order. Hereafter, the feature vectors after rearrangement processing  202  is set to (v 1 , . . . , v D ) (1&lt;=I&lt;=j&lt;=D). 
         [0033]      FIG. 8  is a view of a rearranged vector (v 1 , . . . , v 6 ) for D=6 (after rearrangement processing  202 ). In  FIG. 8 , a vertical axis is a size of the value of each component of the feature vectors, and a horizontal axis is the number of dimensions of each component. 
         [0034]    Initialization processing  203  is a processing of initializing a loop processing by the number of quantization to perform quantization processing  20 . Before explaining initialization processing, signs are defined (1&lt;=I&lt;=j&lt;=D). 
         [0035]    “e i,j ” is the minimum error when quantizing v i , . . . , v j  by quantization number 1. That is, it is the minimum value of the error when replacing it into q. q is a real number; however, it is an average m of v i , . . . , v j  as mentioned later. The error is calculated by the square sum of the difference of each component as shown in the equation (1) described below. 
         [0036]    Next, calculation of e i,j  will be explained. The quantization error when replacing all v i , . . . , v j  with q is shown in the equation (1) described below. 
         [0000]    
       
         
           
             
               
                 
                   
                     
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         [0000]    where m is an average value of v i , . . . , v j , and σ 2  is distribution of v i , . . . , v j . 
         [0037]    According to equation (1), when q is an average m of v i , . . . , v j , the quantization error is at the minimum, and the value is (j−i)σ 2 . 
         [0038]      FIG. 9  is a view of e i,j  of the vector of  FIG. 8  (D=6). “E i,M ” is the minimum error when quantizing v i , . . . , v j  by quantization number M. 
         [0039]      FIG. 10  is a view of the vector E i,M  of  FIG. 8 . “T i,M ” is a set of division values of the quantization threshold, which is at the minimum when quantizing v i , . . . , v j  by quantization number M. The quantization number of value a is defined as follows for the division value group Ti, M={t 1 , . . . , t (M−1) }: 
         [0040]    a quantization number is 1 for a&lt;t 1 , 
         [0041]    a quantization number is i for t (i−1) &lt;=a&lt;t i , and 
         [0042]    a quantization number is M for t (M−1) &lt;=a. 
         [0043]      FIG. 10  is a view of vectors E i,M  and T i,M  of  FIG. 8  (D=6, N=2). That is, T 22 , . . . , T 62  are a set of division values of the minimum binary quantization errors as shown in  FIG. 10 . As shown in  FIG. 10 , E 22 , . . . , E 62  are the binary quantization errors at that time. In each graph of  FIG. 10 , “the size of the value of each component of the feature vectors” of a vertical axis is divided by division value t, and each of the divided section is the quantization threshold. For example, there are two division values and three quantization thresholds, for quantization number N=3. “N” is the quantization number of the quantization function under processing. 
         [0044]    Initialization processing  203  performs the following processing for each i=1, . . . , D. The first processing assigns an empty set to T i,l . The second processing assigns the value of e 1,i  to Ei. The third processing assigns 1 to N. 
         [0045]    The above-mentioned processing may be omitted and a processing of quantization threshold search processing  204  for N=2 as mentioned later may be initialization processing  203 . 
         [0046]    Quantization threshold search processing  204  is a processing of adding 1 to N and calculate E D, N  and T D,N  using T i,(N−1)  and E i,(N−1)  (i=N−1, . . . , D). More specifically, the following processing is performed. 
         [0047]    Calculate 
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         [0048]    Assign T a,(N−1) ∪{v (a+1) } to T D,N    
         [0049]    Assign E a,(N−1) +e (a+1),D  to E D,N    
         [0050]      FIG. 11  illustrates quantization threshold search processing  204  of the vector of  FIG. 8  for N=3. That is, the maximum division value is moved to v 3 , . . . , v 6  and calculates a quantization result with each division value to obtain each quantization error. As a division value which minimizes the quantization error of three-valued quantization, we let t 61 =t 41  and t 62 =v 5 . 
         [0051]    An error/quantization quantification processing  205  moves on to quantization feature vector output processing  207 , when quantization error E D, N  and quantization number N are evaluated and the quantization error and the quantization number meet the standard. On the other hand, when they do not meet the standard, the processing moves onto re-search preparation processing  206 . 
         [0052]    The standard may be “a quantization error is below a predetermined value.” Also, the standard may be “a quantization number corresponds with a predetermined value” by calculating the quantization number from a desired compression rate. 
         [0053]    Re-search preparation processing  206  is a processing of calculating E j,N  and T j, N  (=N, . . . , (D−1)) using T i,(N−1)  and E i,(N−1)  (I=N−1, . . . , D). More specifically, the following processing is performed for each j=N, . . . , (D−1). 
         [0054]    Calculate 
         [0000]    
       
         
           
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         [0055]    Assign T β,(N−1) ∪{v (β+1) } to T j,N    
         [0056]    Assign E β,(N−1) +e (β+1),i  to E j,N    
         [0057]      FIG. 12  illustrates re-search preparation processing  206  of the vector of  FIG. 8  for N=3 and i=5 to calculate T 53 ={t 51 , t 52 } and E 53 . That is, the maximum division value is moved to v 3 , . . . , v 5  and calculates a quantization result with each division value to obtain each quantization error. As a division value which minimizes the quantization error of three-valued quantization of v 1 , . . . , v 5 , we let t 51 =t 31 , t 52 =v 4  and the minimum value be E 53 =E 32 +e 45 . 
         [0058]    Quantization feature vector output processing  207  uses the quantization function which is determined by a set of division values T D, N ={t 1 , . . . , t (N−1) } of the quantization threshold which minimizes the quantization error and quantization values m 1 , . . . , m N  (subscripts are quantization numbers), quantizes each component of the feature vectors to N values and outputs the quantization feature vector. We let the quantization function be a function of outputting the following value for input of real number x. 
         [0059]    M 1  is output when x&lt;t 1 , 
         [0060]    m i  is output when t (i−1) &lt;=x&lt;t i , 
         [0061]    m N  is output when t (N−1) &lt;=X. 
         [0062]    Similarity calculation processing  30  is a similarity calculation processing performed by similarity calculation unit  105 . The flow chart of similarity calculation processing  30  is shown in  FIG. 3 . 
         [0063]    Similarity calculation processing  30  includes quantization feature vector input processing  301 , coefficient table generation processing  302 , coefficient addition processing  303 , and output processing  304 . Each processing will be explained as follows. 
         [0064]    Quantization feature vector input processing  301  is a processing of performing input of an input quantization feature vector and a dictionary quantization feature vector. The “quantization feature vector” is given as a set of array of the quantization value and array of the quantization number of each component. 
         [0065]    Coefficient table generation processing  302  is a processing of generating a coefficient table from the array of each quantization value of the input quantization feature vector and the dictionary quantization feature vector. 
         [0066]    The coefficient table which calculates the distance of the input quantization feature vector and the dictionary quantization feature vector is given by the following M×N matrix C=(c ij ), if the quantization number of each quantization feature vector is M and N, and two quantization values are (q 1 , . . . , q m ) and (r 1 , . . . , r M ), respectively. 
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         [0067]    Coefficient addition processing  303  is a processing of calculating a set corresponding to each component from the array of the quantization number of the input quantization feature vector and the dictionary quantization feature vector and calculates a total of the values of the coefficient table corresponding to the set. 
         [0068]    The following value as shown in equation (3) will be calculated if the dimension of the feature space is D and arrays of two quantization numbers are (m 1 , . . . , m D ) and (n 1 , . . . , n D ), respectively. 
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         [0069]    After the calculation, a root square of the above mentioned value is calculated and the calculated value will be set as the similarity, since the above mentioned value is a square value of the distance between vectors. 
         [0070]    Output processing  304  is a processing of outputting the similarity obtained by coefficient addition processing  303 . 
         [0071]    According to this embodiment, the error caused by compression is suppressed by quantization of the input feature vector and the dictionary feature vector, and an amount of data may be compressed. 
         [0072]    Since the error of the value defined between the input quantization feature vector before and after the compression and the dictionary quantization feature vector may be reduced and degradation of the recognition performance by compression may also be suppressed. 
         [0073]    In similarity calculation processing  30 , a similarity, which is the distance between the feature vectors in a compressed state without decompression, may be calculated 
       Second Embodiment 
       [0074]    Pattern recognition apparatus  40  of a second embodiment of this invention will be explained with reference to  FIGS. 4 and 5 . Pattern recognition apparatus  40  of this embodiment calculates the similarity from an input feature vector corresponding to an input pattern, and a dictionary quantization feature vector which is created by quantizing a dictionary feature vector to be compared and performs pattern recognition from the similarity. 
         [0075]    Pattern recognition apparatus  40  of this embodiment will be explained with reference to  FIG. 4 .  FIG. 4  is a schematic block diagram illustrating a pattern recognition apparatus  40 . Pattern recognition apparatus  40  comprises a pattern input unit  401 , a feature extraction unit  402 , a dictionary feature memory storing unit  403 , a similarity calculation unit  404  and a determination unit  405 . Functions of each unit  401 - 405  may be realized by a program stored in a computer. Functions of each unit  401 - 405  will be explained below. 
         [0076]    Pattern input unit  401  inputs the pattern to be used as a candidate for recognition. When the pattern is an image, an image captured by, for example, a digital camera may be input into a computer or an image captured by a camera connected to a computer may be input into the computer. When the pattern is a sound, for example, a recorded sound may be input into a computer or a sound recorded by a microphone connected to a computer may be input into the computer. 
         [0077]    Feature extraction unit  402  extracts feature values from a pattern input by pattern input unit  401  and converts the pattern into a vector. Hereafter, the vector converted by feature extraction unit  402  is called a “feature vector.” When the input pattern is an image, the pattern is changed into a vector by, for example, a raster scan. When the input pattern is a sound, for example, a vector which has frequency components of the sound within a definite period of time is used. 
         [0078]    After converting into a vector, a processing of suppressing an input pattern change may be performed. For example, a processing of removing a noise component with a small eigen value obtained from the pattern prepared in large quantities beforehand by principal component analysis etc. may be performed. 
         [0079]    Dictionary feature memory storing unit  403  performs a processing by feature extraction unit  402  and a quantization processing  20  for a pattern of each class for recognition and stores the obtained quantization feature vectors (hereinafter, referred to as “dictionary quantization feature vector”) into a storage area. 
         [0080]    Similarity calculation unit  404  calculates a value indicating a similarity between a quantization feature vector of the input pattern generated by feature extraction unit  402  (hereinafter, referred to as “input quantization feature vector”) and a dictionary quantization feature vector of each class stored in dictionary feature memory storing unit  403 . Here, the distance between vectors is calculated. 
         [0081]      FIG. 5  is a flow chart of a similarity calculation processing  50  performed by similarity calculation unit  404 . Explanation of similarity calculation processing  50  is mentioned later. 
         [0082]    Determination unit  405  identifies the class for recognition, when the class has the highest similarity among the registered classes fulfilling conditions of the similarity. When no class fulfills the conditions, determination unit  405  identifies that there is no class in the class. When the distance between vectors is used as the similarity, the distance is set to be smaller than a predetermined threshold and has higher similarity as the distance becomes smaller. 
         [0083]    Similarity calculation processing  50  includes quantization feature vector input processing  501 , feature vector input processing  502 , addition processing  503  which is classified by quantization number, addition result integrated processing  504  and output process  505 . The explanation of each processing is as follows. 
         [0084]    Quantization feature vector input processing  501  is a processing of inputting dictionary quantization feature vector. Here, the dictionary quantization feature vector stored in dictionary feature memory storing unit  403  is input. The “quantization feature vector” is given as a set of array of the quantization value and array of the quantization number of each component. 
         [0085]    Feature vector input processing  502  is a processing of inputting the input feature vector. Here, the input feature vector generated by feature extraction unit  402  is input. 
         [0086]    Addition processing  503 , which is classified by quantization number, is a processing of calculating f i g i h i  as defined below for each i=1, . . . , N, let the quantization number of the dictionary quantization feature vector which is input by quantization feature vector input processing  501  be N, array of quantization values be (q 1 , . . . , q N ), array of quantization number of each component be (n 1 , . . . , n N ) and the input feature vector input by feature vector input processing  502  be (a 1 , . . . , a D ) (Ai={j|n j =i}). 
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         [0087]    Addition result integrated processing  504  is a processing of calculating the following values. 
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         [0088]    After the calculation, a root square of the above mentioned value is calculated, since the above mentioned value is a square value of the distance between vectors. 
         [0089]    Output process  505  is a processing of outputting the value obtained by addition result integrated processing  504 . 
         [0090]    According to this embodiment, the error caused by compression is suppressed by quantization of the input feature vector and the data may be stored in dictionary feature memory storing unit  403  in a compressed state. Since the error of the value defined between the input feature vector before and after compression and the dictionary feature vector may be small, and degradation of the recognition performance by compression may also be suppressed. 
         [0091]    In similarity calculation processing  30 , a similarity, which is the distance between the feature vectors in a compressed state without decompression, may be calculated. 
       Third Embodiment 
       [0092]    Pattern recognition apparatus  60  of a third embodiment of this invention will be explained with reference to  FIGS. 6 and 7 . Pattern recognition apparatus  60  comprises a pattern input unit  601 , a feature extraction unit  602 , a feature vector quantization unit  603 , a dictionary feature storing unit  604 , a similarity calculation unit  605 , and a determination unit  606 . Functions of each unit  601 - 606  may be realized by a program stored in a computer. Functions of each unit  601 - 606  will be explained below. 
         [0093]    Pattern input unit  601  inputs the pattern to be used as a candidate for recognition. When the pattern is an image, an image captured by, for example, a digital camera may be input into a computer or an image captured by a camera connected to a computer may be input into the computer. When the pattern is a sound, for example, a recorded sound may be input into a computer or a sound recorded by a microphone connected to a computer may be input into the computer. 
         [0094]    Feature extraction unit  602  extracts feature values from a pattern input by pattern input unit  601  and converts the pattern into a vector. Hereafter, the vector converted by feature extraction unit  602  is called a “input feature vector.” When the input pattern is an image, the pattern is changed into a vector by, for example, a raster scan. When the input pattern is a sound, for example, a vector which has frequency components of the sound within a definite period of time is used. 
         [0095]    After converting into a vector, a processing of suppressing an input pattern change may be performed. For example, a processing of removing a noise component with a small eigen value obtained from the pattern prepared in large quantities beforehand by principal component analysis etc. may be performed. 
         [0096]    Feature vector quantization unit  603  performs quantization processing of each component of the feature vectors of quantization processing  20  for the input feature vector generated by feature extraction unit  602 . Hereinafter, a quantized input feature vector is referred to as “input quantization feature vector.” 
         [0097]    Dictionary feature storing unit  604  performs processing performed by feature extraction unit  602  for a pattern of each class for recognition and stores generated dictionary feature vectors (hereinafter, referred to as “dictionary feature vector”) into a storage area. 
         [0098]    Similarity calculation unit  605  calculates a value indicating a similarity between a input quantization feature vector of the input pattern output by feature vector quantization unit  603  and a dictionary quantization feature vector of each class stored in dictionary feature storing unit  604 . Here, the distance between vectors is calculated as a similarity between vectors. 
         [0099]    Determination unit  606  identifies the class for recognition, when the class has the highest similarity among the registered classes fulfilling conditions of the similarity. When no class fulfills the conditions, determination unit  606  identifies that there is no class in the class. When the distance between vectors is used as the similarity, the distance is set to be smaller than a predetermined threshold and has higher similarity as the distance becomes smaller. 
         [0100]    According to this embodiment, the error caused by compression is suppressed by the above quantization of the input feature vector and an amount of data may be compressed. 
         [0101]    Since a similarity between the input quantization feature vector before and after the compression and the dictionary quantization feature vector may be reduced and degradation of the recognition performance by compression may also be suppressed. 
         [0102]    In the above similarity calculation processing of the quantization feature vector, a similarity, which is the distance between the feature vectors in a compressed state without decompression, may be calculated. 
         [0103]    This invention is not limited to the above-mentioned embodiments but may be changed variously if it falls within the scope of the invention. 
         [0104]    Quantization error e i,j , which is used by initialization processing  203 , quantization threshold search processing  204  and re-search preparation processing  206 , may be calculated by the sum of the absolute value of the difference of each component. 
         [0105]    In this case, the quantization error is given by the following equation (7) when replacing all components vi, . . . , vj with q (v l &lt;=q l+1 ). 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       
                         
                           
                             ∑ 
                             
                               k 
                               = 
                               i 
                             
                             j 
                           
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                               q 
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                                 v 
                                 k 
                               
                             
                              
                           
                         
                         = 
                           
                          
                         
                           
                             
                               ∑ 
                               
                                 k 
                                 = 
                                 i 
                               
                               l 
                             
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                               ( 
                               
                                 q 
                                 - 
                                 
                                   v 
                                   k 
                                 
                               
                               ) 
                             
                           
                           + 
                           
                             
                               ∑ 
                               
                                 k 
                                 = 
                                 
                                   l 
                                   + 
                                   1 
                                 
                               
                               j 
                             
                              
                             
                               ( 
                               
                                 
                                   v 
                                   k 
                                 
                                 - 
                                 q 
                               
                               ) 
                             
                           
                         
                       
                     
                   
                   
                     
                       
                         = 
                           
                          
                         
                           
                             
                               ( 
                               
                                 
                                   2 
                                    
                                   l 
                                 
                                 + 
                                 1 
                                 - 
                                 
                                   ( 
                                   
                                     i 
                                     + 
                                     j 
                                   
                                   ) 
                                 
                               
                               ) 
                             
                              
                             q 
                           
                           - 
                           
                             
                               ∑ 
                               
                                 k 
                                 = 
                                 i 
                               
                               l 
                             
                              
                             
                               v 
                               k 
                             
                           
                           + 
                           
                             
                               ∑ 
                               
                                 k 
                                 = 
                                 
                                   l 
                                   + 
                                   1 
                                 
                               
                               j 
                             
                              
                             
                               v 
                               k 
                             
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
         [0106]    When i+j is even, the quantization error is at the minimum for p=(i+j−1)/2 and q=v p . The minimum values are given by the following equation. 
         [0000]    
       
         
           
             
               
                 ∑ 
                 
                   k 
                   = 
                   
                     p 
                     + 
                     1 
                   
                 
                 j 
               
                
               
                 v 
                 k 
               
             
             - 
             
               
                 ∑ 
                 
                   k 
                   = 
                   i 
                 
                 
                   p 
                   - 
                   1 
                 
               
                
               
                 v 
                 
                   k 
                    
                   
                       
                   
                 
               
             
           
         
       
     
         [0107]    When i+j is odd, the quantization error is at the minimum for p=(i+j−1)/2. The minimum values are given by the following equation. 
         [0000]    
       
         
           
             
               
                 ∑ 
                 
                   k 
                   = 
                   
                     p 
                     + 
                     2 
                   
                 
                 j 
               
                
               
                 v 
                 k 
               
             
             - 
             
               
                 ∑ 
                 
                   k 
                   = 
                   i 
                 
                 
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                   - 
                   1 
                 
               
                
               
                 v 
                 
                   k 
                    
                   
                       
                   
                 
               
             
           
         
       
     
         [0108]    The above mentioned quantization value, which minimizes the quantization error between each of the quantization thresholds to be used, is used by quantization feature vector output processing  207 . 
         [0109]    The value of the Gaussian kernel equation (8) using the distance between two vectors as a similarity between the vectors calculated by similarity calculation unit  105  may be calculated. 
         [0000]    
       
         
           
             
               
                 
                   exp 
                   ( 
                   
                     
                       
                          
                         
                           x 
                           - 
                           x 
                         
                          
                       
                       2 
                     
                     
                       σ 
                       2 
                     
                   
                   ) 
                 
               
               
                 
                   ( 
                   8 
                   ) 
                 
               
             
           
         
       
     
         [0110]    As a similarity between the vectors calculated by similarity calculation unit  105 , the inner product of two vectors and its square may be calculated. 
         [0111]    If a coefficient table defined by coefficient table generation processing  302  by equation (9) instead of equation (2) is generated when calculating the inner product between the quantization feature vectors, the inner product between vectors may be calculated. 
         [0000]    
       
         
           
             
               
                 
                   C 
                   = 
                   
                     ( 
                     
                       
                         
                           
                             
                               q 
                               1 
                             
                              
                             
                               r 
                               1 
                             
                           
                         
                         
                           … 
                         
                         
                           
                             
                               q 
                               1 
                             
                              
                             
                               r 
                               N 
                             
                           
                         
                       
                       
                         
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                               q 
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                              
                             
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                               1 
                             
                           
                         
                         
                           … 
                         
                         
                           
                             
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                               r 
                               N 
                             
                           
                         
                       
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   9 
                   ) 
                 
               
             
           
         
       
     
         [0112]    Values such as a polynomial kernel equation (10) using this inner product and equation (11) may also be a similarity between vectors, let (u, u′) be an inner product of vector u and u′, and p be one or more integers, the value of p be set by a suitable value by experiment. 
         [0000]      (u,u′) p   (10) 
         [0000]      ((u,u′)+1) p   (11) 
         [0113]    As a similarity between the vectors calculated by similarity calculation unit  105 , sum of the absolute value of the difference of each of two components may be calculated. Hereafter, this value is referred to as L1 distance between vectors. 
         [0114]    If the coefficient table defined by equation (12) instead of equation (2) by coefficient table generation processing  302  is generated when calculating L1 distance between the quantization feature vectors, L1 distance between vectors may be calculated (| | is taken as an absolute value). 
         [0000]    
       
         
           
             
               
                 
                   C 
                   = 
                   
                     ( 
                     
                       
                         
                           
                             ( 
                             
                                
                               
                                 
                                   q 
                                   1 
                                 
                                 - 
                                 
                                   r 
                                   1 
                                 
                               
                                
                             
                           
                         
                         
                           … 
                         
                         
                           
                              
                             
                               
                                 q 
                                 1 
                               
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                                 N 
                               
                             
                              
                           
                         
                       
                       
                         
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                           … 
                         
                         
                           
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                              
                           
                         
                       
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   12 
                   ) 
                 
               
             
           
         
       
     
         [0115]    As a similarity between the vectors calculated by similarity calculation unit  404 , the inner product of two vectors and its square may be calculated. When calculating the inner product between the quantization feature vectors, the inner product between vectors may be obtained by calculating g i  of equation (5) by addition processing  503  which is classified by quantization number, and calculating equation (13) by addition result integrated processing  504 . 
         [0000]    
       
         
           
             
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                    
                   
                     
                       g 
                       i 
                     
                      
                     
                       q 
                       i 
                     
                   
                 
               
               
                 
                   ( 
                   13 
                   ) 
                 
               
             
           
         
       
     
         [0116]    The value of the polynomial kernel equation (10) using this inner product and equation (11) may be used as a similarity between vectors. 
         [0117]    As a similarity between the vectors calculated by similarity calculation unit  404 , L1 distance may be calculated. When calculating L1 distance between the quantization feature vectors, b i  which is defined as shown below by addition processing  503  which is classified by quantization number may be calculated (| | is taken as an absolute value.). 
         [0000]    
       
         
           
             
               
                 
                   
                     b 
                     i 
                   
                   = 
                   
                     
                       ∑ 
                       
                         j 
                         ∈ 
                         
                           A 
                           i 
                         
                       
                     
                      
                     
                        
                       
                         
                           q 
                           i 
                         
                         - 
                         
                           a 
                           j 
                         
                       
                        
                     
                   
                 
               
               
                 
                   ( 
                   14 
                   ) 
                 
               
             
           
         
       
     
         [0118]    Next, L1 distance between vectors may be calculated by calculating equation (14) by addition result integrated processing  504 . 
         [0000]    
       
         
           
             
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                    
                   
                     b 
                     i 
                   
                 
               
               
                 
                   ( 
                   15 
                   ) 
                 
               
             
           
         
       
     
         [0119]    Lossless compression may be used when the quantization feature vector is stored in a storage area of dictionary feature storing unit  104  and dictionary feature memory storing unit  403 . When lossless compression is carried out, the stored quantization feature vector is restored and used in similarity calculation unit  105 . 
         [0120]    For example, Huffman encoding (T. M. Cover and J. A. Thomas and Elements of information Theory.NewYork:Willey.2006 reference) etc. may be used as lossless compression.

Technology Classification (CPC): 6