Image processing method and apparatus using training dictionary

A non-transitory computer-readable storage medium storing an image processing program as a computer program to cause a computer to execute an image process of classifying object images included in a first image into multiple types and producing a second image showing a result of the classification, the image process comprising:

• extracting, from the entire first image, multiple partial areas such that in the first image no area remains which is not extracted as the partial area and such that the partial area are allowed to overlap one another;
• providing, each as a set of bases produced by dictionary learning using model images corresponding to the respective types, a set of linear combination approximation bases to approximate the partial areas by linear combination and a set of classification bases to acquire classification identification values each indicating one of the multiple types to which each partial area belongs;
• approximating each of the partial areas by the linear combination of the linear combination approximation bases to acquire linear combination coefficients;
• setting the classification identification values corresponding to each of the partial areas by a linear combination of the classification bases and the linear combination coefficients;
• setting, for each of pixels of the first image, one classification identification value by using the classification identification value set for two or more of the partial areas each including that pixel;
• and
• producing the second image whose pixels corresponding to the pixels of the first image, each of the pixels of the second image having the one classification identification value as its pixel value.

An image processing apparatus configured to classify object images included in a first image into multiple types and to produce a second image showing a result of the classification, the image processing apparatus comprising:

• an extractor configured to extract, from the entire first image, multiple partial areas such that in the first image no area remains which is not extracted as the partial area and such that the partial area are allowed to overlap one another;
• a memory configured to store, each as a set of bases produced by dictionary learning using model images corresponding to the respective types, a set of linear combination approximation bases to approximate the partial areas by linear combination and a set of classification bases to acquire classification identification values each indicating one of the multiple types to which each partial area belongs;
• an approximator configured to approximate each of the partial areas by the linear combination of the linear combination approximation bases to acquire linear combination coefficients;
• a classifier configured to set the classification identification values corresponding to each of the partial areas by a linear combination of the classification bases and the linear combination coefficients;
• a setter configured to set, for each of pixels of the first image, one classification identification value by using the classification identification value set for two or more of the partial areas each including that pixel;
• and
• a producer configured to produce the second image whose pixels corresponding to the pixels of the first image, each of the pixels of the second image having the one classification identification value as its pixel value.