Multi-class discriminating device, data discrimination device, multi-class discriminating method and data discriminating method

Abstract

A multi-class discriminating device for judging to which class a feature represented by data falls. The device has a first unit for generating plural first hierarchical discriminating devices for discriminating one from N, and a second unit for combining score values output respectively from the plural first hierarchical discriminating devices to generate a second hierarchical feature vector and for entering the second hierarchical feature vector to generate plural second hierarchical discriminating devices for discriminating one from N. When data is entered, the plural first hierarchical discriminating devices output score values, and these score values are combined together to generate the second hierarchical feature vector. When the second hierarchical feature vector is entered, the second hierarchical discriminating device which outputs the maximum score value is selected. The class corresponding to the selected second hierarchical discriminating device is discriminated as the class, into which the feature represented by the entered data falls.

Description

[0001] This application claims the priority of the Japanese patent application No. 2012-236440 for which it applied on October 26, 2012 as a basic application, and takes in all the content of this basic application in this application. technical field [0002] The invention relates to a multi-category identifier, a data identification device, a multi-category identification method and a data identification method. Background technique [0003] There are cases where you want to know the name of a flower found in the mountains or on the roadside. For this reason, the following technology has been proposed (for example, refer to JP-A-2002-203242): Through digital images of flowers and leaves obtained by photography, etc., a large number of local features of flowers and leaves as objects are extracted using a clustering method, Furthermore, a single or a plurality of feature quantities using the histogramized information of the extracted feature groups are obtained as feature qu...

AI Technical Summary

Problems solved by technology

In this way, when a multi-class classifier is constructed by combining two class classifiers, there is a problem that the classification performance of each class becomes uneven, that is, the normalization is not performed.

[0006] Furthermore, when a multi-class classifier is formed by combining 2-class classifiers, if a sufficient amount of learning data is not given to each 2-class classifier, the recognition performance of each class will become uneven.

However, for example, in the classification of flower types, there is a problem that it is difficult to provide a sufficient amount of learning data for all classifications.

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