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Regular version of the site

Article

Bayes Classifier Based on Tree Structured Gaussian Mixtures

Pattern Recognition and Image Analysis. 2012. Vol. 22. No. 1. P. 136-143.
Lange, M., Novikov A. Nikita.
A novel approach is proposed to constructing a Bayes classifier in a multidimensional space of fea tures by using treestructured Gaussian mixtures as estimates of classconditional probability density func tions. A training procedure is developed for the classifier that is reduced to finding numbers of mixture com ponents and their thresholds in order to realize rejections for the given classes. The mixture parameters are optimized by a crossvalidation method. Classification error rate is estimated on a set of 3D vectors of textual features of a monochrome image. Comparative error rates are obtained for classifiers that use histograms, individual Gaussian densities, and Gaussian mixtures constructed using the EM (expectationmaximization) algorithm. The practical application of the developed classifier is illustrated by results of image segmentation for a satellite picture. The image represents a fragment of the Earth surface and it is obtained using the Google Earth program.