Abstract: An information processing method of convenience and an information processing system are disclosed. In some embodiments, the system includes acquiring a 1st attribute information from a person with a personal digital assistant that contains a 2nd attribute information; acquiring the 2nd attribute information from two or more personal digital assistants; comparing the acquired 1st attribute information with the acquired 2nd attribute information to form countervalue information; and storing the countervalue information in the personal digital assistant of the person from which the 1st attribute information was acquired.

Abstract: An information processing method of convenience and an information processing system are disclosed. In some embodiments, the system includes acquiring a 1st attribute information from a person with a personal digital assistant that contains a 2nd attribute information; acquiring the 2nd attribute information from two or more personal digital assistants; comparing the acquired 1st attribute information with the acquired 2nd attribute information to form countervalue information; and storing the countervalue information in the personal digital assistant of the person from which the 1st attribute information was acquired.

Abstract: A feature amount obtained from an image group for registration is stored as dictionary data in a memory. When an input image group constituted of a plurality of images is supplied to be collated with the dictionary data, the images constituting the input image group are classified based on a specific classification standard, a feature amount obtained from each of the classified image groups is collated with the feature amount stored as the dictionary data in the memory, and a result of the collation of all the input image groups with the dictionary data is determined based on a result of integrating a plurality of collection results of the classified image groups with the dictionary data.

Abstract: A video discrimination apparatus executes learning processing by acquiring a plurality of sample video pictures and information indicating a category of each sample video picture, classifying sample video pictures of each category into subcategories, determining a subcategory with a closest relation to each sample video picture for each combination of subcategories, which are selected one each from the respective categories, and calculating, for each combination of subcategories, a video discrimination parameter based on the frequency of occurrence of matches between a category to which the subcategory determined to have the closest relation to each sample video picture belongs and a category of that sample video picture. The video discrimination apparatus executes video discrimination processing for classifying video pictures into categories based on the integration result of a plurality of video discrimination parameters obtained by the learning processing.

Abstract: A feature amount obtained from an image group for registration is stored as dictionary data in a memory. When an input image group constituted of a plurality of images is supplied to be collated with the dictionary data, the images constituting the input image group are classified based on a specific classification standard, a feature amount obtained from each of the classified image groups is collated with the feature amount stored as the dictionary data in the memory, and a result of the collation of all the input image groups with the dictionary data is determined based on a result of integrating a plurality of collection results of the classified image groups with the dictionary data.