Full text of article: Russian language.
Abstract:
This paper presents a problem of reducing the dimensionality of a  feature space in recognition problems on images and proposes a certain  problem-solving technique. The proposed method allows us to reduce a number of  features required to solve a specific recognition problem, from several hundred  thousand of features (number of pixels of the original image) to a few tens or  hundreds of features. The developed method consists of three stages. At the  first stage, we calculate two-dimensional maps of features from a training  sample for each image (for example, spatial filters processing results,  spectral features), and in these maps we preselect the features by the  total-to-average intraclass variance criterion. Then the selection is performed  by searching different combinations (by the method of sequential addition and  deletion of features) using the criterion of a specific recognition problem for  which the features are selected. At the last stage, selected combinations of  the features are tested on a control image sample, and a final decision on  selection of a feature set is made in order to use it in recognition. The  proposed method was successfully applied in selection of the features for  implementation of the test person recognition system by face images on documents.
Key words:
feture space reduction, intraclass variance criterion, person  recognition system, face images.
Citation:
Glumov N.I., Myasnikov E.V. Method of the informative features selection on  the digital images [In Russian]. Computer Optics 2007; 31(3): 73-76.
References:
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