Color Images Segmentation using Pairwise Markov Chain

Abstract

In this paper, we present two comparative studies. The first one is between two hidden stationaries models of Markov using in image segmentation such as Hidden Markov Chain with Independent Noise (HMC-IN) and Pairwise Markov Chain (PMC). The second one is between three parameter estimators such as EM (Exceptation-Maximization) algorithm, ICE (Iterative Conditional Estimation) algorithm and SEM (Stochastic Exceptation-Maximization) algorithm. To estimate the final configuration of X, we have used MPM (Marginal Posteriori Mode) algorithm. From these comparisons, we can confirm that PMC provides better results of segmentation than HMC-IN. Moreover, EM, ICE, SEM give the same results under HMC-IN and PMC.