Abstract:
Disclosed herein is an information processing apparatus configured to classify time-series input data into N classes, including, a time-series feature quantity extracting section, N calculating sections, and a determination section.

Description:
CROSS REFERENCES TO RELATED APPLICATIONS 
     The present application claims priority from Japanese Patent Application No. JP 2007-294313, filed in the Japanese Patent Office on Nov. 13, 2007, the entire content of which is incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to an information processing apparatus, an information processing method, an a program and, more particularly, to an information processing apparatus, an information processing method, and a program that are suitably for use in the identification of video contents of video data represented by television programs, for example. 
     2. Description of the Related Art 
     For example, methods are proposed for identifying the video contents in order to automatically generate digests of television programs and automatically extract highlight scenes. 
     The video contents of time-series video data of television programs for example are identified by a method that uses one of probabilistic models, or HMM (Hidden Markov Model) that is able to use time-series data as a target of processing (refer to “Automatic Indexing for Baseball Broadcast based on Hidden Markov Model,” Image Recognition and Understanding Symposium (MIRU2005), July 2005 by Nguyen Huu Back, Koichi Shinoda, Sada Furui, hereinafter referred to as Non-Patent Document 1 for example) 
     Non-Patent Document 1 describes a method of identifying the video contents of baseball live coverage by use of HMM. To be specific, the HMMs corresponding to the video contents (for example, pitching scene, homerun scene, infield grounder scene, walking scene, strikeout scene, and so on) of a baseball live coverage are generated by learning in advance and the video data of a baseball live coverage is supplied to each learned HMM, thereby recognizing a scene corresponding to the HMM having a largest output likelihood value, as the video contents of the baseball live coverage. 
     Each HMM outputs a likelihood value that the video data to be entered is indicative of a corresponding scene. For example, the HMM corresponding to a homerun scene outputs a likelihood value that the video data to be entered is indicative of a homerun scene. 
     SUMMARY OF THE INVENTION 
     Related-art video identification techniques based on the above-mentioned HMM can recognize video contents. However, these related-art techniques sometimes involve the erroneous recognition of video contents, thereby requiring a novel technique that is capable of identifying video contents with higher accuracy than before. 
     Therefore, the present invention addresses the above-identified and other problems associated with related-art methods and apparatuses and solves the addressed problems by providing an information processing apparatus, an information processing method, and a computer program that are capable of identifying video contents with higher accuracy. 
     According to an embodiment of the present invention there is provided an information processing apparatus configured to classify time-series input data into N classes. This above-mentioned information processing apparatus has time-series feature quantity extracting means for extracting a time-series feature quantity of the time-series input data; N calculating means for calculating, by applying the extracted time-series feature quantity to a probabilistic model learned in advance, likelihood values that the time-series input data belongs to any one of the N classes; and determination means for determining, by applying one of patterns of N dimension and dimensions higher than N that includes the calculated N likelihood values to pattern identification sections learned in advance, whether the time-series input data belongs to which of the N classes. 
     In the above-mentioned information processing apparatus, the time-series input data is video data and the N classes are scenes of N different types that are video contents of the video data. 
     The information processing apparatus further has non-time-series feature quantity extracting means for extracting a non-time-series feature quantity of the time-times input data. In this information processing apparatus, the determination means, by applying (N+M)-dimension patterns including the N calculated likelihood values and M extracted non-time-series feature quantities to a pattern identification section learned in advance, determines whether the time-series input data belongs to which of the N classes. 
     In the above-mentioned processing apparatus, the probabilistic model is a Hidden Markov Model and the pattern identification section is a neural network. 
     According to another embodiment of the present invention there is provided an information processing method for an information processing apparatus configured to classify time-series input data into N classes. The above-mentioned information processing method has the steps of: extracting a time-series feature quantity of the time-series input data; calculating, by applying the extracted time-series feature quantity to a probabilistic model learned in advance, likelihood values that the time-series input data belongs to any one of the N classes; and determining, by applying one of patterns of N dimension and dimensions higher than N that includes the calculated N likelihood values to pattern identification sections learned in advance, whether the time-series input data belongs to which of the N classes. 
     According to still another embodiment of the present invention, there is provided a program for controlling an information processing apparatus configured to classify time-series input data into N classes. The above-mentioned program has the steps of: extracting a time-series feature quantity of the time-series input data; calculating, by applying the extracted time-series feature quantity to a probabilistic model learned in advance, likelihood values that the time-series input data belongs to any one of the N classes; and determining, by applying one of patterns of N dimension and dimensions higher than N that includes the calculated N likelihood values to pattern identification sections learned in advance, whether the time-series input data belongs to which of the N classes. 
     According to an embodiment of the present invention, a time-series feature quantity of time-series input data is extracted. The extracted time-series feature quantity is applied to a probabilistic model that has been learned in advance to calculate a likelihood value that the time-series input data belongs to any one of N classes. In addition, patterns of N or higher dimensions including the calculated N classes are applied to pattern identification sections that have been learned in advance to determine whether the time-series input data belong to which of the N classes. 
     Embodiments of the present invention allow the classification of time-series input data with significantly high accuracy. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating a first exemplary configuration of a video data identification apparatus practiced as one embodiment of the invention; 
         FIG. 2  is a block diagram illustrating an exemplary configuration of a time-series learning apparatus configured to make a by-scene HMM identification block shown in  FIG. 1  learn; 
         FIG. 3  is a flowchart indicative of learning processing corresponding to the video data identification apparatus shown in  FIG. 1 ; 
         FIG. 4  is a flowchart indicative of scene identification processing to be executed by the video data identification apparatus shown in  FIG. 1 ; 
         FIG. 5  is a block diagram illustrating a second exemplary configuration of a video data identification apparatus practiced as one embodiment of the invention; 
         FIG. 6  is a flowchart indicative of learning processing of the video data identification apparatus shown in  FIG. 5 ; 
         FIG. 7  is a flowchart indicative of scene identification processing to be executed by the video data identification apparatus shown in  FIG. 5 ; and 
         FIG. 8  is a block diagram illustrating an exemplary configuration of a general-purpose computer. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     This invention will be described in further detail by way of embodiments thereof with reference to the accompanying drawings. 
     Now referring to  FIG. 1 , there is shown an exemplary configuration of a video data identification apparatus practiced as a first embodiment of the invention. This video data identification apparatus  10  processes video data, such as television programs that are entered in a time-series manner, identifying the time-series data of video contents of entered video data. The video data identification apparatus  10  is made up of a time-series identification section  11  and a pattern identification section  12 . 
     The following describes the identification of video contents (a pitching scene, a homerun scene, an infield grounder scene, a walking scene, a strikeout scene, and so on) of the video data of a baseball live coverage program, for example. 
     The time-series identification section  11  is configured to identify video data by use of HMM for example and is made up of a time-series feature quantity extraction block  21  and a plurality of by-scene HIM identification blocks  22 - 1  through  22 -N. 
     The time-series feature quantity extraction block  21  divides video data into predetermined intervals (for example, several seconds or several frames) and extracts feature quantities based on move quantity, image histogram and main component analysis, a fractal feature quantity, and an inter-frame luminance difference feature quantity, for example. The extracted time-series feature quantities are supplied to the by-scene HMM identification blocks  22 - 1  through  22 -N. 
     The by-scene HMM identification block  22 - 1  computes a likelihood value that a time series feature quantity (to be described later) was learned in advance in correspondence with one scene (a pitching scene for example) supposed as the video contents of video data and a time-series feature quantity entered from the time-series feature quantity extraction block  21  is that of a corresponding scene (a pitching scene in this case). 
     The by-scene HMM identification block  22 - 2  computes a likelihood value that a time series feature quantity (to be described later) was learned in advance in correspondence with one scene (a homerun scene for example) supposed as the video contents of video data and a time-series feature quantity entered from the time-series feature quantity extraction block  21  is that of a corresponding scene (a homerun scene in this case). 
     Likewise, the by-scene HMM identification blocks  22 - 3  through  22 -N compute a likelihood value that a time series feature quantity (to be described later) was learned in advance in correspondence with different scenes supposed as the video contents of video data and time-series feature quantities entered from the time-series feature quantity extraction block  21  is those of corresponding scenes. 
     Therefore, the time-series identification section  11  outputs N-types of likelihood values as information indicative whether the video contents of entered video data are supposed N-types of scenes or not. 
     The pattern identification section  12  executes pattern recognition by use of a neural network (hereafter also referred to as NN) and is made up of an input pattern generating block  31  and a scene decision block  32 . 
     The input pattern generating block  31  generates an N-dimension input patterns on the basis of N likelihood values entered from the by-scene HMM identification blocks  22 - 1  through  22 -N of the time-series identification section  11  and outputs the generated input patterns to the scene decision block  32 . The previously learned scene decision block  32  computes the likelihood values of the N-types of scenes of the N-dimension input patterns supplied from the input pattern generating block  31  and outputs a scene corresponding to the greatest of the obtained values as a video content recognition result. It should be noted that the learning of the scene decision block  32  can be made by a back propagation algorithm for example by use of learning video data (with time-series scenes identified by man). 
     Referring to  FIG. 2 , there is shown an exemplary configuration of a time-series learning apparatus  40  for learning the by-scene HMM identification blocks  22 - 1  through  22 -N shown in  FIG. 1  by use of learning video data. 
     The time-series learning apparatus  40  is made up of a time-series feature quantity extraction block  41 , an operator block  42 , a selector  43 , and by-scene HMM learning blocks  44 - 1  through  44 -N. 
     The time-series feature quantity extraction block  41 , like the time-series feature quantity extraction block  21  shown in  FIG. 1 , divides video data into predetermined intervals (for example, several seconds or several frames) and extracts feature quantities based on move quantity, image histogram and main component analysis, a fractal feature quantity, and an inter-frame luminance difference feature quantity, for example, and outputs the extracted feature quantities to the selector  43 . 
     The operator block  42  is operated by an operator (or a user) who identify learning video data scenes for example. A scene identification result is supplied to the selector  43  through the operator block  42 . In response to the scene identification result supplied from the operator through the operator block  42 , the selector  43  supplies a time-series feature quantity supplied from the time-series feature quantity extraction block  41  to one of the by-scene HMM learning blocks  44 - 1  through  44 -N. It should be noted that the by-scene HMM learning blocks  44 - 1  through  44 -N are respectively related to different video contents (a pitching scene, a homerun scene, an infield grounder scene, a walking scene, a strikeout scene, and so on). 
     For example, assume that the by-scene HMM learning block  44 - 1  is related to a pitching scene, the by-scene HMM learning block  44 - 2  is related to a homerun scene, and the by-scene HMM learning block  44 - 3  is related to an infield grounder scene. Then, if the video contents of learning video data is identified by the operator to be a homerun scene and the operator block  42  is operated accordingly, the selector  43  supplies the time-series feature quantity of that scene to the by-scene HMM learning block  44 - 2 . If the video contents of learning video data is identified by the operator to be an infield grounder scene and the operator block  42  is operated accordingly, the selector  43  supplies the time-series feature quantity of that scene to the by-scene HMM learning block  44 - 3 . 
     The by-scene HMM learning block  44 - 1  through  44 -N learn HMM on the basis of the time-series feature quantity supplied via the selector  43 . For this learning, the Baum-Welch algorithm can be used. Then, the learning is repeatedly executed by use of two or more different learning video data until the identification by the by-scene HMM learning blocks  44 - 1  through  44 -N has reached a desired accuracy. When the identification is found reaching a desired accuracy, the final HMM of the by-scene HMM learning blocks  44 - 1  through  44 -N is applied to the by-scene HMM identification blocks  22 - 1  through  22 -N of the time-series identification section  11  shown in  FIG. 1 . 
     The following describes the previous learning processing for the video data identification apparatus  10  to be able to identify video data scenes more accurately, with reference to a flowchart shown in  FIG. 3 . 
     First, in steps S 1  through S 3 , the by-scene HMM identification blocks  22 - 1  through  22 -N of the time-series identification section  11  are learned. 
     To be more specific, in step S 1 , the time-series feature quantity extraction block  41  of the time-series learning apparatus  40  divides the learning video data into predetermined intervals to extract a time-series feature quantity of each interval and outputs the extracted time-series feature quantities to the selector  43 . 
     In step S 2 , in response to a result of scene identification made by the user through the operator block  42 , the selector  43  supplies the time-series feature quantity supplied from the time-series feature quantity extraction block  41  to one of the by-scene HMM learning blocks  44 - 1  through  44 -N. On the basis of the time-series feature quantity supplied from the selector  43 , the by-scene HMM learning blocks  44 - 1  through  44 -N learn HMM. 
     In step S 3 , it is determined whether the identification by the by-scene HMM learning blocks  44 - 1  through  44 -N has reached a desired accuracy. Until a desired accuracy is reached, the processes of steps S 1  through S 3  are repeatedly executed. If the identification by the by-scene HMM learning blocks  44 - 1  through  44 -N is found reaching a desired accuracy in step S 3 , the final HMM of the by-scene HMM learning blocks  44 - 1  through  44 -N is applied to the by-scene HMM identification blocks  22 - 1  through  22 -N of the time-series identification section  11  shown in  FIG. 1 . Then, the procedure goes to step S 4 . 
     In steps S 4  through S 8 , the scene decision block  32  of the pattern identification section  12  is learned. 
     To be more specific, in step S 4 , a time-series feature quantity is extracted from the learning video data and the extracted time-series feature quantity is supplied to the by-scene HMM identification blocks  22 - 1  through  22 -N learned in the above-mentioned steps S 1  through S 3 . 
     In step S 5 , the by-scene HMM identification blocks  22 - 1  through  22 -N compute likelihood values that the supplied time-series feature quantities corresponds to supposed scenes and output the obtained likelihood values to the input pattern generating block  31 . In step S 6 , the input pattern generating block  31  generates an N-dimension input patterns on the basis of N likelihood values entered from the by-scene HMM identification blocks  22 - 1  through  22 -N and outputs the generated N-dimension input patterns to the scene decision block  32 . 
     In step S 7 , the scene decision block  32  learns NN on the basis of the N-dimension input patterns supplied from the input pattern generating block  31  and the result of the scene identification by the operator who viewed the learning video data. 
     In step S 8 , it is determined whether the identification by the scene decision block  32  has reached a desired accuracy or not. The processes of steps S 4  through  8  are repeatedly executed until a desired accuracy is reached. If the identification by the scene decision block  32  is found reaching a desired accuracy in step S 8 , then the learning processing comes to an end. 
     The following describes video data scene identification processing by the video data identification apparatus  10  including the by-scene HMM identification blocks  22 - 1  through  22 -N and the scene decision block  32  that have been learned by the above-mentioned learning processing, with reference to a flowchart shown in  FIG. 4 . 
     In step S 11 , the time-series feature quantity extraction block  21  of the time-series identification section  11  divides the video data subject to processing into predetermined intervals to extract time-series feature quantities thereof. In step S 12 , the time-series feature quantity extraction block  21  supplies the extracted time-series feature quantities to the by-scene HMM identification blocks  22 - 1  through  22 -N. The by-scene HMM identification blocks  22 - 1  through  22 -N compute the likelihood value that the supplied time-series feature quantities are of the corresponding scenes (pitching scene, homerun scene, infield grounder scene, walking scene, strikeout scene, and so on). The obtained likelihood values are supplied to the input pattern generating block  31  of the pattern identification section  12 . 
     In step S 13 , the input patterns generating block  31  generates an N-dimension input patterns on the basis of the N likelihood values entered from the by-scene HMM identification blocks  22 - 1  and  22 -N of the time-series identification section  11  and outputs the generated N-dimension input patterns. 
     In step S 14 , the scene decision block  32  computes a likelihood value of each of the N-types of scenes of the N-dimension input patterns entered from the input patterns generating block  31  and outputs a scene corresponding to the greatest value of the obtained likelihood values as a video content identification result. 
     Thus, the scene identification processing by the video data identification apparatus  10  has been described. As described, the video data identification apparatus  10  identifies video data scenes not by use of HMM, but by the pattern decision based N likelihood value patterns outputted from two or more HMMs, so that chances of error decision can be reduced, thereby enhancing the accuracy of identification. 
     The following describes an exemplary configuration of the video data identification apparatus practiced as a second embodiment of the invention. This video identification apparatus  70  is made up of substantially the same time-series identification section  11  of the video data identification apparatus  10  shown in  FIG. 1 , non-time-series feature extraction blocks  71 - 1  through  71 -N configured to extract non-time-series feature quantities from video data subject to processing, and a pattern identification section  72 . 
     The non-time-series feature quantity extraction blocks  71 - 1  through  71 -N divides the video data subject to processing into predetermined intervals (for example, several seconds or several frames), extracts a representative image pattern, a representative color, a representative object on the screen, and so on, as non-time-series feature quantities, and outputs the extracted information to the pattern identification section  72 . 
     The pattern identification section  72  executes pattern identification by use of NN, for example, and is made up of an input pattern generating block  81  and a scene decision block  82 . 
     The input pattern generating block  81  generates (N+M)-dimension input patterns on the basis of N likelihood values entered from the by-scene HMM identification blocks  22 - 1  through  22 -N of the time-series identification section  11  and M non-time-series feature quantities entered from the non-time-series feature quantity extraction blocks  71 - 1  through  71 -M and outputs the generated input patterns to the scene decision block  82 . The scene decision block  82  learned in advance computes the likelihood values of N-types of scenes of the (N+M) input patterns entered from the input pattern generating block  81  and outputs the scene corresponding to the greatest value of the obtained likelihood values as a video contents identification result. It should be noted that the learning of scene decision block  82  can be executed by a back propagation algorithm, for example, by use of learning video data (with time-series scenes identified by man). 
     The following describes the learning processing to be executed in advance so as for the video identification apparatus  70  to identify video data scenes more accurately, with reference to a flowchart shown in  FIG. 6 . 
     First, like the processes of step S 1  through S 3  shown in  FIG. 6  above, the by-scene HMM identification blocks  22 - 1  through  22 -N of the time-series identification section  11  are learned by the processes of steps S 31  through S 33 . 
     Next, in steps S 34  through  39 , the scene decision block  82  of the pattern identification section  72  is learned. 
     To be more specific, in step S 34 , a time-series feature quantity is extracted from the learning video data and the extracted time-series feature quantity is supplied to the by-scene HMM identification blocks  22 - 1  through  22 -N learned in steps S 31  through S 33 . 
     In step S 35 , the by-scene HMM identification blocks  22 - 1  through  22 -N compute a likelihood value that the supplied time-series feature quantities correspond to supposed scenes and outputs the obtained likelihood value to the input pattern generating block  81  of the pattern identification section  72 . 
     In step S 36 , the non-time-series feature quantity extraction blocks  71 - 1  through  71 -M divide the learning video data into predetermined intervals to extract non-time-series feature quantities thereof and outputs the extracted non-time-series feature quantities to the input pattern generating block  81  of the pattern identification section  72 . 
     In step S 37 , the input pattern generating block  81  generates (N+M)-dimension input patterns on the basis of the N likelihood values entered from the by-scene HMM identification blocks  22 - 1  through  22 -N and the non-time-series feature quantity extraction blocks  71 - 1  through  71 -M and outputs the generated input patterns to the scene decision block  82 . 
     In step S 38  the scene decision block  82  learns NN on the basis of the (N+M)-dimension input patterns entered from the input pattern generating block  81  and the result of the scene identification by the operator who viewed the learning video data. 
     In step S 39 , it is determined whether the identification by the scene decision block  82  has reached a desired accuracy or not. The processes of steps S 34  through S 39  are repeatedly executed until a desired accuracy is reached. If the identification by the scene decision block  82  is found reaching a desired accuracy in step S 39 , then this learning processing comes to an end. 
     The following describes video data scene identification processing to be executed by the video data identification apparatus  70  including the by-scene HMM identification block  22 - 1  through  22 -N and the scene decision block  82  that have been learned by the above-mentioned learning processing, with reference to a flowchart shown in  FIG. 7 . 
     In step S 51 , the time-series feature quantity extraction block  21  of the time-series identification section  11  divides the video data subject to processing into predetermined intervals to extract time-series feature quantities thereof. In step S 52 , the time-series feature quantity extraction block  21  supplies the extracted time-series feature quantities to the by-scene HMM identification blocks  22 - 1  through  22 -N. The by-scene HMM identification block  22 - 1  through  22 -N compute the likelihood value that the supplied time-series feature quantities are those of corresponding scenes (a pitching scene, a homerun scene, an infield grounder scene, a walking scene, a strikeout scene, and so on). The obtained likelihood value is supplied to the input pattern generating block  81  of the pattern identification section  72 . 
     In step  53 , the non-time-series feature quantity extraction blocks  71 - 1  through  7 -M divide the video data subject to processing into predetermined intervals to extract non-time-series feature quantities hereof and outputs the extracted non-time-series feature quantities to the input pattern generating block  81 . 
     In step S 54 , the input pattern generating block  81  generates (N+M)-dimension patterns on the basis of N likelihood values entered from the by-scene HMM identification blocks  22 - 1  through  22 -N and M non-time-series feature quantities entered from the non-time-series feature quantity extraction blocks  71 - 1  through  71 M and outputs the generated patterns to the scene decision block  82 . 
     In step S 55 , the scene decision block  82  computes the likelihood values of the N-types of scenes of the (N+M)-dimension input patterns entered from the input pattern generating block  81  and outputs the scene corresponding to the greatest value of the obtained likelihood values as a video contents identification result. 
     Thus, the scene identification processing executed by the video data identification apparatus  70  has been described. As described, the video data identification apparatus  10  identifies video data scenes not by use of HMM, but by the pattern decision based on the patterns of N likelihood values and M non-time-series feature quantities outputted from the HMMs, so that the chances of erroneous identification can be reduced as compared with the identification based on only HMM, thereby enhancing the accuracy of identification. The above-mentioned novel configuration also allows the scene identification by use of non-time-series feature quantities. 
     As described above, HMM is used for the time-series identification section  11  in the above-mentioned embodiments of the invention; however, it is also practicable to use other probabilistic models other than HMM. As described above, NN is used for the pattern identification section  12  and the pattern identification section  72 ; however it is also practicable to use other pattern recognition algorithm than NN. 
     It should be noted that the embodiments of the present invention is applicable to not only the scene identification of video data, but also the classification of time-series data of given types. 
     The above-mentioned sequence of processing operations may be executed by software as well as hardware. When the above-mentioned sequence of processing operations is executed by software, the programs constituting the software are installed in a computer which is built in dedicated hardware equipment or installed, from a network or recording media, into a general-purpose personal computer for example in which various programs may be installed for the execution of various functions. 
     Referring to  FIG. 8 , there is shown a block diagram illustrating an exemplary hardware configuration of a computer configured to execute the above-mentioned sequence of processing operations by software programs. 
     In this computer  100 , a CPU (Central Processing Unit)  101 , a ROM (Read Only Memory)  102 , a RAM (Random Access Memory)  103  are interconnected by a bus  104 . 
     The bus  104  is further connected to an input/output interface  105 . The input/output interface  105  is connected to an input block  106  having a keyboard, a mouse, a microphone, and so on, an output block  107  having a display monitor, a loudspeaker, and so on, a storage block  108  based on hard disk or a nonvolatile memory, a communication block  109  based on a network interface, and a drive  110  for driving a removable media  111 , such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, for example. 
     In the computer  100  thus configured, the CPU  101  loads programs stored in the storage  108  into the RAM  103  via the input/output interface  105  and the bus  104  so as to execute the above-mentioned sequence of processing operations, for example. 
     It should be noted herein that the steps for describing each program recorded in recording media include not only the processing operations which are sequentially executed in a time-dependent manner but also the processing operations which are executed concurrently or discretely. 
     It should also be noted that programs may be executed by a single unit of computer or two or more units of computer in a distributed manner or transferred to a remote computer for execution. 
     While preferred embodiments of the present invention have been described using specific terms, such description is for illustrative purpose only, and it is to be understood that changes and variations may be made without departing from the spirit or scope of the following claims.