Information processing apparatus, information processing method, and non-transitory recording medium

An information processing method that is executed by at least one computer, the information processing comprising:

• acquiring time-series image data;
• calculating an integrated feature quantity or a score, the integrated feature quantity being obtained by integrating a first feature quantity that is a feature quantity of the time-series image data and a second feature quantity that is a feature quantity of registered image data registered in advance, the score indicating a degree of similarity between the first feature quantity and the second feature quantity;
• calculating N first likelihood ratios, each indicating a likelihood that the time-series image data belong to respective one of N registered classes (where N is a natural number) corresponding to the registered image data, based on the integrated feature quantity or the score;
• calculating a second likelihood ratio indicating a likelihood that the time-series image data belong to an unregistered class in a case where the time-series image data are not registered in advance, based on the N first likelihood ratios;
• and
• determining that the time-series image data belong to the registered class in a case where the first likelihood ratio reaches a predetermined threshold, and determining that the time-series image data do not belong to any registered class in a case where the second likelihood ratio reaches the predetermined threshold.

A non-transitory recording medium on which a computer program that allows at least one computer to execute an information processing method is recorded, the information processing method including:

• acquiring time-series image data;
• calculating an integrated feature quantity or a score, the integrated feature quantity being obtained by integrating a first feature quantity that is a feature quantity of the time-series image data and a second feature quantity that is a feature quantity of registered image data registered in advance, the score indicating a degree of similarity between the first feature quantity and the second feature quantity;
• calculating N first likelihood ratios, each indicating a likelihood that the time-series image data belong to respective one of N registered classes (where N is a natural number) corresponding to the registered image data, based on the integrated feature quantity or the score;
• calculating a second likelihood ratio indicating a likelihood that the time-series image data belong to an unregistered class in a case where the time-series image data are not registered in advance, based on the N first likelihood ratios;
• and
• determining that the time-series image data belong to the registered class in a case where the first likelihood ratio reaches a predetermined threshold, and determining that the time-series image data do not belong to any registered class in a case where the second likelihood ratio reaches the predetermined threshold.