METHOD FUZZY CLUSTERING k-MEANS WITH SMOOTHING PENALTY FUNCTION
Abstract
A new method of clustering of grayscale, color and multispectral images is presented. It is based on conditional optimization of the objective function consisting of the classic fuzzy functional criterion and the penalty function of Gibbs type, which controls local smoothness of the solution. The method provides more smooth solutions that are essentially more precise in comparison with fuzzy c-means results in the case of noisy images.
About the Author
B. A. Zalesky
Объединенный институт проблем информатики НАН Беларуси
Belarus
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For citations:
Zalesky B.A.
METHOD FUZZY CLUSTERING k-MEANS WITH SMOOTHING PENALTY FUNCTION. Informatics. 2014;(3):14-20.
(In Russ.)
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