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
References
1. Steinhaus, H. Sur la division des corps materiels en parties / H. Steinhaus // Bull. Acad. Po-lon. - 1956. - Vol. 4 (12). - P. 801-804.
2. Lloyd, S. Least squares quantization in PCM / S. Lloyd // IEEE Transactions on Information Theory. - 1982. - Vol. 28, no. 2. - P. 129-137.
3. Bezdek, J.C. Pattern Recognition with Fuzzy Objective Function Algoritms / J.C. Bezdek. - MA, USA : Kluwer Academic Publishers Norwell, 1981. - 256 p.
4. Gonsales, R. Tsifrovaya obrabotka izobrazhenii / R. Gonsales, R. Vuds. - M. : Tekhnosfe-ra, 2005. - 1075 s.
5. Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition / F. Höppner [et al.]. - N. Y. : John Wiley & Sons, 1999. - 300 p.
6. MacQueen, J.B. Methods for classification and Analysis of Multivariate Observations / J.B. MacQueen // Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability. - Berke-ley, 1967. - P. 281-297.
7. Genitha, C.H. Classification of satellite images using new fuzzy cluster centroid for unsuper-vised classification algorithm // C.H. Genitha, K. Vani // Proc. IEEE Conf. on Information and Com-munication Technologies ICT2013. - JeJu Island, 2013. - P. 203-207.
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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