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Texture Segmentation Using Statistical Characteristics of SOM and Multiscale Bayesian Image Segmentation Technique  

Kim Tae-Hyung (Dept. of Electronics Eng., Pusan Univ.)
Eom Il-Kyu (Dept. of Information and Communication Eng., Miryang Univ.)
Kim Yoo-Shin (Dept. of Electronics Eng., Pusan Univ.)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.42, no.6, 2005 , pp. 43-54 More about this Journal
Abstract
This paper proposes a novel texture segmentation method using Bayesian image segmentation method and SOM(Self Organization feature Map). Multi-scale wavelet coefficients are used as the input of SOM, and likelihood and a posterior probability for observations are obtained from trained SOMs. Texture segmentation is performed by a posterior probability from trained SOMs and MAP(Maximum A Posterior) classification. And the result of texture segmentation is improved by context information. This proposed segmentation method shows better performance than segmentation method by HMT(Hidden Markov Tree) model. The texture segmentation results by SOM and multi-sclae Bayesian image segmentation technique called HMTseg also show better performance than by HMT and HMTseg.
Keywords
Texture segmentation; SOM; Wavelets; Multi-scale Bayesian image segmentation; HMT;
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