Information processing apparatus, information processing method, and program

An information processing method comprising:

• by using an ensemble classifier, which includes a plurality of weak classifiers outputting weak hypotheses which indicates whether a predetermined subject is shown in an image, in response to inputs of a first plurality of features extracted from the image, and a second plurality of features extracted from an input image,
• sequentially integrating the weak hypotheses output by the weak classifiers in regard to the first and second plurality of features and distinguishing whether the predetermined subject is shown in the input image based on the integrated value of the weak hypotheses by an information processing apparatus,
• wherein each weak classifier classifies each of the first and second plurality of features to one of three or more sub-divisions based on threshold values, calculates sum divisions of the sub-divisions of the first and second plurality of features as whole divisions into which the first and second plurality of features is classified, and outputs, as one of the weak hypotheses, a reliability degree of the whole divisions assignable to the whole divisions of the first and second plurality of features,
• wherein the ensemble classifier is calculated by ensemble learning, and
• wherein in the ensemble learning, the reliability degree of the whole divisions is assignable to the respective whole divisions of each weak classifier.

A non-transitory computer-readable medium including a program, which when executed by a computer, causes the computer to function as:

• a distinguishing unit which, by using an ensemble classifier, which includes a plurality of weak classifiers outputting weak hypotheses which indicates whether a predetermined subject is shown in an image, in response to inputs of a first plurality of features extracted from the image, and a second plurality of features extracted from an input image, sequentially integrates the weak hypotheses output by the weak classifiers in regard to the first and second plurality of features and distinguishes whether the predetermined subject is shown in the input image based on the integrated value of the weak hypotheses,
• wherein each weak classifier classifies each of the first and second plurality of features to one of three or more sub-divisions based on threshold values, calculates sum divisions of the sub-divisions of the first and second plurality of features as whole divisions into which the first and second plurality of features is classified, and outputs, as one of the weak hypotheses, a reliability degree of the whole divisions assignable to the whole divisions of the first and second plurality of features,
• wherein the ensemble classifier is calculated by ensemble learning, and
• wherein in the ensemble learning, the reliability degree of the whole divisions is assignable to the respective whole divisions of each weak classifier.