Markovian Segmentation Of Textured Color Images

Abstract

This paper presents an application of textured color images segmentation using hidden Markov chain model. We propose two comparative studies, The first one is between EM(Expectation-Maximization) algorithm,SEM (Stochastic Expectation-Maximization) algorithm and ICE (Iterative Conditional Estimation) algorithm, these estimators are used to estimate the parameters of Hidden Markov Chain with Independent Noise. The second one is between MAP(Maximum a Posteriori) and MPM(Maximum Marginal Posteriori) algorithms. These strategies are used to estimate the resulted images of segmentation. The obtained results show that EM algorithm, SEM, and ICE algorithm give the same results of segmentation under MAP and MPM algorithms. But, MPM provides better segmentation results than MAP.