Patent Publication Number: US-2012045117-A1

Title: Method and device for training, method and device for estimating posture visual angle of object in image

Description:
TECHNICAL FIELD 
     The present invention relates to object posture estimation, and especially to a training method and a training apparatus for purpose of object posture orientation estimation, and a method and an apparatus for estimating the posture orientation of an object in an image. 
     BACKGROUND 
     Methods of estimating the posture of an object (e.g., human, animal, object or the like) in a single image may be divided into model based and learning based according to their technical principles. According to the learning based methods, three dimensional (3-D) postures of objects are directly deduced from image features. An often used image feature is object outline information. 
     Posture orientations of objects are not distinguished in the existing methods for object posture estimation. Because of complexity of object posture variation, different posture orientations of objects may bring about further ambiguity in the estimation. Therefore, accuracy of image posture estimation under different orientations is far lower than that of the posture estimation under one orientation. 
     SUMMARY 
     In view of the above deficiencies of the prior art, the present invention is intended to provide a method and an apparatus for training based on input images, and a method and an apparatus for estimating a posture orientation of an object in an image, to facilitate distinguishing object posture orientations in the object posture estimation. 
     An embodiment of the present invention is a method of training based on input images, including: extracting an image feature from each of a plurality of input images each having an orientation class; with respect to each of a plurality of orientation classes, estimating a mapping model for transforming image features extracted from input images of the orientation class into 3-D object posture information corresponding to the input images through a linear regression analysis; and calculating a joint probability distribution model based on samples obtained by connecting the image features with their corresponding 3-D object posture information, wherein single probability distribution models which the joint probability distribution model is based on correspond to different orientation classes, and each of the single probability distribution models is based on samples including the image features extracted from the input images of a corresponding orientation class. 
     Another embodiment of the present invention is an apparatus for training based on input images, including: An extracting unit which extracts an image feature from each of a plurality of input images each having an orientation class; a map estimating unit which, with respect to each of a plurality of orientation classes, estimates a mapping model for transforming image features extracted from input images of the orientation class into 3-D object posture information corresponding to the input images through a linear regression analysis; and a probability model calculating unit which calculates a joint probability distribution model based on samples obtained by connecting the image features with their corresponding 3-D object posture information, wherein single probability distribution models which the joint probability distribution model is based on correspond to different orientation classes, and each of the single probability distribution models is based on samples including the image features extracted from the input images of a corresponding orientation class. 
     According to the embodiments of the present invention, the input images have the respective orientation classes. It is possible to extract an image feature from each input image. Based on the orientation class, it is possible to estimate the mapping model through the linear regression analysis. Such mapping model acts as a function for converting image features of the orientation class to the corresponding 3-D object posture information. It is possible to connect the image feature with the corresponding 3-D object posture information to obtain a sample, so as to calculate the joint probability distribution model based on these samples. The joint probability distribution model is based on a number of single probability distribution models, where each orientation class has one single probability distribution model. Based on the samples including image features of the respective orientation class, it is possible to obtain a corresponding single probability distribution model. Therefore, according to the embodiments of the present invention, it is possible to train a model for object posture orientation estimation, that is, the mapping model and the joint probability distribution model for the posture orientations. 
     Further, in the embodiments, it is possible to calculate a feature transformation model for reducing dimensions of the image features with a dimension reduction method. Accordingly, it is possible to transform the image features by using the feature transformation model, for use in estimating the mapping model and calculating the joint probability distribution model. The image feature transformed through the feature transformation model may have a smaller number of dimensions, facilitating the reduction of subsequent processing cost for estimation and calculation. 
     Another embodiment of the present invention is a method of estimating a posture orientation of an object in an image, including: Extracting an image feature from an input image; with respect to each of a plurality of orientation classes, obtaining 3-D object posture information corresponding to the image feature based on a mapping model corresponding to the orientation class, for mapping the image feature to the 3-D object posture information; calculating a joint probability of a joint feature including the image feature and the corresponding 3-D object posture information for each of the orientation classes according to a joint probability distribution model based on single probability distribution models for the orientation classes; calculating a conditional probability of the image feature in condition of the corresponding 3-D object posture information based on the joint probability; and estimating the orientation class corresponding to the maximum of the conditional probabilities as the posture orientation of the object in the input image. 
     Another embodiment of the present invention is an apparatus for estimating a posture orientation of an object in an image, including: an extracting unit which extracts an image feature from an input image; a mapping unit which, with respect to each of a plurality of orientation classes, obtains 3-D object posture information corresponding to the image feature based on a mapping model corresponding to the orientation class, for mapping the image feature to the 3-D object posture information; a probability calculating unit which calculates a joint probability of a joint feature including the image feature and the corresponding 3-D object posture information for each of the orientation classes according to a joint probability distribution model based on single probability distribution models for the orientation classes, and calculates a conditional probability of the image feature in condition of the corresponding 3-D object posture information based on the joint probability; and an estimating unit which estimates the orientation class corresponding to the maximum of the conditional probabilities as the posture orientation of the object in the input image. 
     According to the embodiments of the present invention, it is possible to extract an image feature from the input image. Because each orientation class has a corresponding mapping model for converting the image feature of the orientation class to 3-D object posture information, it is possible to assume that the image feature has the orientation classes respectively, so as to obtain the 3-D object posture information corresponding to the image feature by using the corresponding mapping model. According to the joint probability distribution model, it is possible to calculate joint probabilities that the image feature and the corresponding 3-D object posture information occur in the assumption of the orientation classes respectively. According to the joint probabilities, it is possible to calculate conditional probabilities that the image feature occurs in condition that the corresponding 3-D object posture information occurs. It can be seen that, the orientation class assumption corresponding to the maximum conditional probability may be estimated as the posture orientation of the object in the input image. Therefore, according to the embodiments of the present invention, it is possible to estimate the object posture orientation. 
     Further, in the embodiments, it is possible to transform the image feature with a feature transformation model for dimension reduction to obtain the 3-D object posture information. The image feature transformed through the feature transformation model may have a smaller number of dimensions, facilitating the reduction of subsequent processing cost for mapping and probability calculation. 
     Posture orientations of objects are not distinguished in the existing methods for object posture estimation. Because of complexity of object posture variation, different posture orientations of objects may bring about great ambiguity in the estimation. Therefore, accuracy of image posture estimation under different orientations is far lower than that of the posture estimation under one orientation. An object of the present invention is to estimate the orientation of objects in images and videos, so as to further estimate the object posture under a single orientation. According to experimental results, the present invention can estimate the posture of objects in images and videos effectively. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The above and/or other aspects, features and/or advantages of the present invention will be easily appreciated in view of the following description by referring to the accompanying drawings. In the accompanying drawings, identical or corresponding technical features or components will be represented with identical or corresponding reference numbers. 
         FIG. 1  is a block diagram illustrating the structure of an apparatus for training based on input images according to an embodiment of the present invention. 
         FIG. 2  is a schematic diagram for illustrating a scheme of extracting blocks from an input image. 
         FIG. 3  is a flow chart illustrating a method of training based on input images according to an embodiment of the present invention. 
         FIG. 4  is a block diagram illustrating the structure of an apparatus for training based on input images according to a preferable embodiment of the present invention. 
         FIG. 5  is a flow chart illustrating a method of training based on input images according to a preferable embodiment of the present invention. 
         FIG. 6  is a block diagram illustrating the structure of an apparatus for estimating the posture orientation of an object in an image according to an embodiment of the present invention. 
         FIG. 7  is a flow chart illustrating a method of estimating the posture orientation of an object in an image according to an embodiment of the present invention. 
         FIG. 8  is a block diagram illustrating the structure of an apparatus for estimating the posture orientation of an object in an image according to a preferable embodiment of the present invention. 
         FIG. 9  is a flow chart illustrating a method of estimating the posture orientation of an object in an image according to a preferable embodiment of the present invention. 
         FIG. 10  is a block diagram showing the exemplary structure of a computer for implementing the embodiments of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The embodiments of the present invention are below described by referring to the drawings. It is to be noted that, for purpose of clarity, representations and descriptions about those components and processes known by those skilled in the art but unrelated to the present invention are omitted in the drawings and the description. 
       FIG. 1  is a block diagram illustrating the structure of an apparatus  100  for training based on input images according to an embodiment of the present invention. 
     As illustrated in  FIG. 1 , the apparatus  100  includes an extracting unit  101 , a map estimating unit  102  and a probability model calculating unit  103 . 
     The input images are those including objects having various posture orientation classes. The posture orientation classes represent different orientations assumed by the objects respectively. For example, the posture orientation classes may include −80°, −40°, 0°, +40° and +80°, where −80° is a posture orientation class representing that the object turns to right by 80 degree relative to the lens of the camera, −40° is a posture orientation class representing that the object turns to right by 40 degree relative to the lens of the camera, 0° is a posture orientation class representing that the object faces to the lens of the camera, +40° is a posture orientation class representing that the object turns to left by 40 degree relative to the lens of the camera, and +80° is a posture orientation class representing that the object turns to left by 80 degree relative to the lens of the camera. 
     Of course, the posture orientation classes may also represent orientation ranges. For example, the 180° range from the orientation in which the object faces to the left side to the orientation in which the object faces to the right side is divided into 5 orientation ranges: [−90°, −54°], [−54°, −18°], [−18°, 18°], [18°, 54°], [54°, 90°], that is, 5 posture orientation classes. 
     The number of the posture orientation classes and specific posture orientations represented by the classes may be set arbitrarily as required, and are not limited to the above example. 
     In an embodiment of the present invention, the input images and the corresponding posture orientation classes are supplied to the apparatus  100 . 
     Preferably, the input images include object images containing no background but with various posture orientations, and object images containing background and with various posture orientations. 
     The extracting unit  101  extracts an image feature from each of a plurality of input images each having an orientation class. The image feature may be various features for object posture estimation. Preferably, the image feature is a statistical feature relating to edge directions in the input images, for example, gradient orientation histogram (HOG) feature and scale invariant feature transform SIFT feature. 
     In a specific example, it is assumed that the gradient orientation histogram feature is adopted as the image feature, and the input images have the same width and the same height (120 pixels×100 pixels). However, the embodiments of the present invention are not limited to the assumed specific feature and size. 
     In this example, the extracting unit  101  may calculate gradients in the horizontal direction and in the vertical direction for each pixel in the input images, that is, 
       Horizontal gradient: I   x ( x,y )= d ( I ( x,y ))/ dx=I ( x+ 1 ,y )− I ( x− 1 ,y )
 
       Vertical gradient: I   y ( x,y )= d ( I ( x,y ))/ dy=I ( x,y+ 1)− I ( x,y− 1)
 
     where I(x, y) represents the grey scale value of a pixel, x and y respectively represent coordinates of the pixel in the horizontal direction and the vertical direction. 
     Then, the extracting unit  101  may calculate the gradient orientation and the gradient intensity of each pixel in the input images according to gradients in the horizontal direction and in the vertical direction for the pixel. 
     Gradient orientation: θ(x,y)=argtg(|I y /I x |) 
     Gradient intensity: Grad(x,y)=√{square root over (I x   2 +I y   2 )} 
     where the range of the gradient orientation θ(x,y) is [0, π]. 
     In this example, the extracting unit  101  may extract 24 blocks of size 32×32 one by one from left to right and from top to bottom, where there are 6 blocks in each row of the horizontal direction, and there are 4 blocks in each column of the vertical direction. Any two blocks adjacent in the horizontal direction or the vertical direction overlap with each other by one-half of them. 
       FIG. 2  is a schematic diagram for illustrating a scheme of extracting blocks from an input image.  FIG. 2  illustrates three blocks  201 ,  202  and  203  of size 32×32. The block  202  overlaps with the block  201  in the vertical direction by 16 pixels, and the block  203  overlaps with the block  201  in the horizontal direction by 16 pixels. 
     The extracting unit  101  may divide each 32×32 block into 16 small blocks of size 8×8, where there are 4 small blocks in each row of the horizontal direction, and there are 4 small blocks in each column of the vertical direction. The small blocks are arranged in the horizontal direction and then in the vertical direction. 
     For each small block of 8×8, the extracting unit  101  calculates a gradient orientation histogram for 64 pixels in the small block, where the gradient orientations are divided into 8 direction bins, that is, every π/8 in the range from 0 to π may be one direction bin. That is to say, for each of the 8 direction bins, a sum of gradient intensities of the pixels having the gradient orientations falling within the direction bin is calculated based on 64 pixels of every small blocks of 8×8, thus obtaining an 8-dimension vector. Accordingly, a 128-dimension vector is obtained for each 32×32 block. 
     For each input image, the extracting unit  101  obtains an image feature by connecting the vector of each block in sequence, and therefore the number of dimensions in the image feature is 3072, that is, 128×24=3072. 
     It is to be noted that, the embodiments of the present invention is not limited to the division scheme and the specific numbers of the blocks and the small blocks in the above examples, and may also adopt other division schemes and specific numbers. The embodiments of the present invention is not limited to the method of extracting features in the above example, and may also adopt other methods of extracting image features for object posture estimation. 
     Returning to  FIG. 1 , with respect to each of the plurality of orientation classes, the map estimating unit  102  estimates a mapping model for converting image features extracted from input images of the orientation class into 3-D object posture information corresponding to the input images through a linear regression analysis. That is to say, for each posture orientation class, it is assumed that there is a certain functional or mapping relation by which the image features extracted from the input images of the posture orientation class can be converted or mapped to the 3-D object posture information corresponding to the input images. Through the linear regression analysis, it is possible to estimate such functional or mapping relation, i.e., mapping model based on the extracted image features and the corresponding 3-D object posture information. 
     For each input image, 3-D object posture information corresponding to the posture of an object contained in the input image is prepared in advance. 
     In a specific example, the image feature (feature vector) extracted from an input image is represented as X m , where m is the number of dimensions of the image feature. All the image features extracted from n input images are represented as a matrix X m×n . Further, 3-D object posture information (vector) corresponding to the extracted image feature X m  is represented as Y p , where p is the number of dimensions of the 3-D object posture information. 3-D object posture information corresponding to all the image features extracted from n input images is represented as a matrix Y p×n . 
     Assuming that Y p×n =A p×m ×X m×n , it is possible to calculate A p×m  such that (Y p×n −A p×m ×X m×n ) 2  is minimum through a linear regression analysis, e.g., a least square method. A p×m  is the mapping model. 
     Returning to  FIG. 1 , the probability model calculating unit  103  calculates a joint probability distribution model based on samples obtained by connecting the image features with their corresponding 3-D object posture information, wherein single probability distribution models which the joint probability distribution model is based on correspond to different orientation classes, and each of the single probability distribution models is based on samples including the image features extracted from the input images of a corresponding orientation class. 
     That is to say, the joint probability distribution model is based on the single probability distribution models for different orientation classes. Through a known method, it is possible to calculate a corresponding single probability distribution model (i.e., model parameters) based on a set of samples of each orientation class, and it is also possible to calculate a joint probability distribution model (i.e., model parameters) for the single probability distribution models of all the posture orientation classes. 
     Suitable joint probability distribution models include, but not limited to, a Gaussian mixture model, a Hidden Markov Model and a Conditional Random Field. 
     In a specific example, the Gaussian mixture model is adopted. In this example, a joint feature (i.e., sample) [X,Y] T  is formed by an image feature (vector) X and 3-D object posture information (vector) Y. It is assumed that the joint feature [X,Y] T  meets a probability distribution equation: 
     
       
         
           
             
               
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     where M is the number of the posture orientation classes, N(x|u i ,Σ i ) is the single Gauss model for posture orientation class i, i.e., a normal distribution model. u i  and Σ i  are parameters of the normal distribution model, ρ i  represents the weight of the single Gauss model for posture orientation class i in a Gaussian mixture model. It is possible to calculate optimal ρ i , u i  and Σ i , i=1, . . . , M, i.e., the mapping model through a known estimating method, e.g., an Expectation-Maximization method (EM) based on a set of joint features for all the posture orientation classes. 
       FIG. 3  is a flow chart illustrating a method  300  of training based on input images according to an embodiment of the present invention. 
     As shown in  FIG. 3 , the method  300  starts from step  301 . At step  303 , an image feature is extracted from each of a plurality of input images each having an orientation class. The input images and the posture orientation classes may be that described in the above with reference to the embodiment of  FIG. 1 . The image feature may be various features for object posture estimation. Preferably, the image feature is a statistical feature relating to edge directions in the input images, for example, gradient orientation histogram (HOG) feature and scale invariant feature transform SIFT feature. 
     At step  305 , with respect to each of the plurality of orientation classes, a mapping model for converting image features extracted from input images of the orientation class into 3-D object posture information corresponding to the input images is estimated through a linear regression analysis. That is to say, for each posture orientation class, it is assumed that there is a certain functional or mapping relation by which the image features extracted from the input images of the posture orientation class can be converted or mapped to the 3-D object posture information corresponding to the input images. Through the linear regression analysis, it is possible to estimate such functional or mapping relation, i.e., mapping model based on the extracted image features and the corresponding 3-D object posture information. 
     For each input image, 3-D object posture information corresponding to the posture of an object contained in the input image is prepared in advance. 
     In a specific example, the image feature (feature vector) extracted from an input image is represented as X m  where m is the number of dimensions of the image feature. All the image features extracted from n input images are represented as a matrix X m×n . Further, 3-D object posture information (vector) corresponding to the extracted image feature X m  is represented as Y p , where p is the number of dimensions of the 3-D object posture information. 3-D object posture information corresponding to all the image features extracted from n input images is represented as a matrix Y p×n . 
     Assuming that Y p×n =A p×m , X m×n , it is possible to calculate A p×m  such that (Y p×n −A p×m ×X m×n ) 2  is minimum through a linear regression analysis, e.g., a least square method. A p×m  is the mapping model. If there are Q orientation classes, Q corresponding mapping models may be generated. 
     Then at step  307 , a joint probability distribution model is calculated based on samples obtained by connecting the image features with their corresponding 3-D object posture information, wherein single probability distribution models which the joint probability distribution model is based on correspond to different orientation classes, and each of the single probability distribution models is based on samples including the image features extracted from the input images of a corresponding orientation class. 
     That is to say, the joint probability distribution model is based on the single probability distribution models for different orientation classes. Through a known method, it is possible to calculate a corresponding single probability distribution model (i.e., model parameters) based on a set of samples of each orientation class, and it is also possible to calculate a joint probability distribution model (i.e., model parameters) for the single probability distribution models of all the posture orientation classes. 
     Suitable joint probability distribution models include, but not limited to, a Gaussian mixture model, a Hidden Markov Model and a Conditional Random Field. 
     In a specific example, the Gaussian mixture model is adopted. In this example, a joint feature (i.e., sample) [X,Y] T  is formed by a image feature (vector) X and 3-D object posture information (vector) Y. It is assumed that the joint feature [X,Y] T  meets a probability distribution equation: 
     
       
         
           
             
               
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     where M is the number of the posture orientation classes, N(x|u i ,Σ i ) is the single Gauss model for posture orientation class i, i.e., a normal distribution model. u i  and Σ i  are parameters of the normal distribution model, ρ i  represents the weight of the single Gauss model for posture orientation class i in a Gaussian mixture model. It is possible to calculate optimal ρ i , u i  and Σ i , i=1, . . . , M, i.e., the mapping model through a known estimating method, e.g., an Expectation-Maximization method (EM) based on a set of joint features for all the posture orientation classes. 
     Then the method  300  ends at step  309 . 
       FIG. 4  is a block diagram illustrating the structure of an apparatus  400  for training based on input images according to a preferable embodiment of the present invention. 
     As illustrated in  FIG. 4 , the apparatus  400  includes an extracting unit  401 , a map estimating unit  402 , a probability model calculating unit  403 , a transformation model calculating unit  404  and a feature transforming unit  405 . The extracting unit  401 , the map estimating unit  402  and the probability model calculating unit  403  have the same functions with the extracting unit  101 , the map estimating unit  102  and the probability model calculating unit  103  in  FIG. 1  respectively, and will not be described in detail here. It is to be noted that, however, the extracting unit  401  is configured to output the extracted image features to the transformation model calculating unit  404  and the feature transforming unit  405 , and the image features input into the map estimating unit  402  and the probability model calculating unit  403  are output from the feature transforming unit  405 . 
     The transformation model calculating unit  404  calculates a feature transformation model for reducing dimensions of the image features by using a dimension reduction method. The dimension reduction method comprises, but not limited to, principle component analysis, factor analysis, single value decomposition, multi-dimensional scaling, locally linear embedding, isomap, linear discriminant analysis, local tangent space alignment, and maximum variance unfolding. The obtained feature transformation model may be used to transform the image features extracted by the extracting unit  401  into image features with less dimensions. 
     In a specific example, the image feature (feature vector) extracted from an input image is represented as X m , where m is the number of dimensions of the image feature. All the image features extracted from n input images are represented as a matrix X m×n . It is possible to calculate a matrix Map d×m  based on the image features X m×n  through the principle component analysis method, where d&lt;m. 
     The feature transforming unit  405  transforms the image features by using the feature transformation model, for use in estimating the mapping model and calculating the joint probability distribution model. For example, in the previous example, it is possible to calculate the transformed image features through the following equation: 
         X′   d×n =Map d×m   ×X   m×n . 
     The transformed image features (the number of dimensions is d) are supplied to the map estimating unit  402  and the probability model calculating unit  403 . 
     In the above embodiment, because the image features transformed with the feature transformation model have less dimensions, it is advantageous for reducing subsequent processing cost for estimation and calculation. 
       FIG. 5  is a flow chart illustrating a method  500  of training based on input images according to a preferable embodiment of the present invention. 
     As shown in  FIG. 5 , the method  500  starts from step  501 . At step  502 , as in step  303  of the method  300 , an image feature is extracted from each of a plurality of input images each having an orientation class. 
     At step  503 , a feature transformation model for reducing dimensions of the image features extracted at step  502  is calculated through a dimension reduction method. The dimension reduction method comprises, but not limited to, principle component analysis, factor analysis, single value decomposition, multi-dimensional scaling, locally linear embedding, isomap, linear discriminant analysis, local tangent space alignment, and maximum variance unfolding. The obtained feature transformation model may be used to transform the extracted image features into image features with less dimensions. 
     In a specific example, the image feature (feature vector) extracted from an input image is represented as X m , where m is the number of dimensions of the image feature. All the image features extracted from n input images are represented as a matrix X m×n . It is possible to calculate a matrix Map d×m  based on the image features X m×n  through the principle component analysis method, where d&lt;m. 
     At step  504 , the image features are transformed by using the feature transformation model, for use in estimating the mapping model and calculating the joint probability distribution model. For example, in the previous example, it is possible to calculate the transformed image features through the following equation: 
         X″   d×n =Map d×m   ×X   m×n . 
     At step  505 , as in step  305  of the method  300 , with respect to each of the plurality of orientation classes, a mapping model for converting image features (already transformed) extracted from input images of the orientation class into 3-D object posture information corresponding to the input images is estimated through a linear regression analysis. 
     Then at step  507 , as in step  307  of the method  300 , a joint probability distribution model is calculated based on samples obtained by connecting the image features (already transformed) with their corresponding 3-D object posture information, wherein single probability distribution models which the joint probability distribution model is based on correspond to different orientation classes, and each of the single probability distribution models is based on samples including the image features extracted from the input images of a corresponding orientation class. 
     Then the method  500  ends at step  509 . 
       FIG. 6  is a block diagram illustrating the structure of an apparatus  600  for estimating the posture orientation of an object in an image according to an embodiment of the present invention. 
     As illustrated in  FIG. 6 , the apparatus  600  includes an extracting unit  601 , a mapping unit  602 , a probability calculating unit  603  and an estimating unit  604 . 
     The extracting unit  601  extracts an image feature from an input image. The input image has the same specification as that of the input images described in the above with reference to the embodiment of  FIG. 1 . The image feature and the method of extracting the image feature are the same as the image features and the extracting method (as described in the above with reference to the embodiment of  FIG. 1 ) which the adopted mapping model is based on. 
     With respect to each of a plurality of orientation classes, the mapping unit  602  obtains 3-D object posture information corresponding to the image feature based on a mapping model corresponding to the orientation class, for mapping the image feature to the 3-D object posture information. The mapping model is that described in the above with reference to the embodiment of  FIG. 1 . Here, for an image feature X m  extracted from the input image, where m is the number of dimensions of the image feature, the mapping unit  602  assumes that all the orientation classes are possible for the input image. Accordingly, with respect to each assumed orientation class, the mapping unit  602  obtains corresponding 3-D object posture information Y p×n =A p×m ×X m  with the corresponding mapping model A p×m . 
     The probability calculating unit  603  calculates a joint probability of a joint feature including the image feature and the corresponding 3-D object posture information for each of the orientation classes according to a joint probability distribution model based on single probability distribution models for the orientation classes, and calculates a conditional probability of the image feature in condition of the corresponding 3-D object posture information based on the joint probability. The joint probability distribution model is that described in the above with reference to the embodiment of  FIG. 1 . That is to say, for each assumed orientation class, the probability calculating unit  603  forms a joint feature [X,Y] T  with the image feature X and the corresponding 3-D object posture information Y, and calculates the joint probability value p([X,Y] T ) of the joint feature [X,Y] T  with the joint probability distribution model. Based on the obtained joint probability value p([X,Y] T ), the probability calculating unit  603  calculates a conditional probability p(Y|X), i.e., p(Y|X)=p([X,Y] T )/∫p([X,Y] T )dX according to the Bayesian theorem for example. 
     The estimating unit  604  estimates the orientation class corresponding to the maximum of the conditional probabilities p(Y|X) calculated for all the possible orientation classes as the posture orientation of the object in the input image. 
       FIG. 7  is a flow chart illustrating a method  700  of estimating the posture orientation of an object in an image according to an embodiment of the present invention. 
     As shown in  FIG. 7 , the method  700  starts from step  701 . At step  703 , an image feature is extracted from an input image. The input image has the same specification as that of the input images described in the above with reference to the embodiment of  FIG. 1 . The image feature and the method of extracting the image feature are the same as the image features and the extracting method (as described in the above with reference to the embodiment of  FIG. 1 ) which the adopted mapping model is based on. 
     At step  705 , with respect to each of a plurality of orientation classes, 3-D object posture information corresponding to the image feature is obtained based on a mapping model corresponding to the orientation class, for mapping the image feature to the 3-D object posture information. The mapping model is that described in the above with reference to the embodiment of  FIG. 1 . Here, for an image feature X m  extracted from the input image, where m is the number of dimensions of the image feature, at step  705 , it is assumed that all the orientation classes are possible for the input image. Accordingly, at step  705 , with respect to each assumed orientation class, corresponding 3-D object posture information Y p×n =A p×m ×X m  is obtained with the corresponding mapping model A p×m . 
     At step  707 , a joint probability of a joint feature including the image feature and the corresponding 3-D object posture information for each of the orientation classes is calculated according to a joint probability distribution model based on single probability distribution models for the orientation classes, and a conditional probability of the image feature in condition of the corresponding 3-D object posture information is calculated based on the joint probability. The joint probability distribution model is that described in the above with reference to the embodiment of  FIG. 1 . That is to say, at step  707 , for each assumed orientation class, a joint feature [X,Y] T  is formed with the image feature X and the corresponding 3-D object posture information Y, and the joint probability value p([X,Y] T ) of the joint feature [X,Y] T  is calculated with the joint probability distribution model. Based on the obtained joint probability value p([X,Y] T ), a conditional probability p(Y|X), i.e., p(Y|X)=p([X,Y] T )/∫p([X,Y] T )dX is calculated according to the Bayesian theorem for example. 
     At step  708 , the orientation class corresponding to the maximum of the conditional probabilities p(Y|X) calculated for all the possible orientation classes is estimated as the posture orientation of the object in the input image. The method  700  ends at step  709 . 
       FIG. 8  is a block diagram illustrating the structure of an apparatus  800  for estimating the posture orientation of an object in an image according to a preferable embodiment of the present invention. 
     As illustrated in  FIG. 8 , the apparatus  800  includes an extracting unit  801 , a transforming unit  805 , a mapping unit  802 , a probability calculating unit  803  and an estimating unit  804 . The extracting unit  801 , the mapping unit  802 , the probability calculating unit  803  and the estimating unit  804  have the same functions with the extracting unit  601 , the mapping unit  602 , the probability calculating unit  603  and the estimating unit  604  in the embodiment of  FIG. 6  respectively, and will not be described in detail here. It is to be noted that, however, the extracting unit  801  is configured to output the extracted image feature to the transforming unit  805 , and the image feature input into the mapping unit  802  and the probability calculating unit  803  is output from the transforming unit  805 . 
     The transforming unit  805  transforms the image feature through a feature transformation model for dimension reduction to obtain the 3-D object posture information. The feature transformation model may be that described in the above with reference to the embodiment of  FIG. 4 . 
     In the above embodiment, because the image feature transformed with the feature transformation model has less dimensions, it is advantageous for reducing subsequent processing cost for mapping and calculation. 
       FIG. 9  is a flow chart illustrating a method  900  of estimating the posture orientation of an object in an image according to a preferable embodiment of the present invention. 
     As shown in  FIG. 9 , the method  900  starts from step  901 . At step  903 , as in step  703 , an image feature is extracted from an input image. 
     At step  904 , the image feature is transformed through a feature transformation model for dimension reduction to obtain the 3-D object posture information. The feature transformation model may be that described in the above with reference to the embodiment of  FIG. 4 . 
     At step  905 , as in step  705 , with respect to each of a plurality of orientation classes, 3-D object posture information corresponding to the image feature is obtained based on a mapping model corresponding to the orientation class, for mapping the image feature to the 3-D object posture information. 
     At step  907 , as in step  707 , a joint probability of a joint feature including the image feature and the corresponding 3-D object posture information for each of the orientation classes is calculated according to a joint probability distribution model based on single probability distribution models for the orientation classes, and a conditional probability of the image feature in condition of the corresponding 3-D object posture information is calculated based on the joint probability. 
     At step  908 , as in step  708 , the orientation class corresponding to the maximum of the conditional probabilities calculated for all the possible orientation classes is estimated as the posture orientation of the object in the input image. The method  900  ends at step  909 . 
     Although the embodiments of the present invention are described with respect to images in the above, the embodiments of the present invention may also be applied to videos, where the videos are processed as sequences of images. 
       FIG. 10  is a block diagram showing the exemplary structure of a computer for implementing the embodiments of the present invention. 
     In  FIG. 10 , a central processing unit (CPU)  1001  performs various processes in accordance with a program stored in a read only memory (ROM)  1002  or a program loaded from a storage section  1008  to a random access memory (RAM)  1003 . In the RAM  1003 , data required when the CPU  1001  performs the various processes or the like is also stored as required. 
     The CPU  1001 , the ROM  1002  and the RAM  1003  are connected to one another via a bus  1004 . An input/output interface  1005  is also connected to the bus  1004 . 
     The following components connected to input/output interface  1005 : An input section  1006  including a keyboard, a mouse, or the like; An output section  1007  including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), or the like, and a loudspeaker or the like; The storage section  1008  including a hard disk or the like; and a communication section  1009  including a network interface card such as a LAN card, a modem, or the like. The communication section  1009  performs a communication process via the network such as the interne. 
     A drive  1010  is also connected to the input/output interface  1005  as required. A removable medium  1011 , such as a magnetic disk, an optical disk, a magnet-optical disk, a semiconductor memory, or the like, is mounted on the drive  1010  as required, so that a computer program read therefrom is installed into the storage section  1008  as required. 
     In the case where the above-described steps and processes are implemented by the software, the program that constitutes the software is installed from the network such as the interne or the storage medium such as the removable medium  1011 . 
     One skilled in the art should note that, this storage medium is not limit to the removable medium  1011  having the program stored therein as illustrated in  FIG. 10 , which is delivered separately from the approach for providing the program to the user. Examples of the removable medium  1011  include the magnetic disk, the optical disk (including a compact disk-read only memory (CD-ROM) and a digital versatile disk (DVD)), the magneto-optical disk (including a mini-disk (MD)), and the semiconductor memory. Alternatively, the storage medium may be ROM  1002 , the hard disk contained in the storage section  1008 , or the like, which have the program stored therein and is deliver to the user together with the method that containing them. 
     The present invention is described in the above by referring to specific embodiments. One skilled in the art should understand that various modifications and changes can be made without departing from the scope as set forth in the following claims.