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Bayes Classifier Based on Tree Structured Gaussian Mixtures
Pattern Recognition and Image Analysis. 2012. Vol. 22. No. 1. P. 136–143.
Lange, M. 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.
Language:
English