Unsupervised Non Stationary Image Segmentation Using Triplet Markov Chains

This work deals with the unsupervised Bayesian hidden Markov chain restoration extended to the non stationary case. Unsupervised restoration based on “ExpectationMaximization” (EM) or “Stochastic EM” (SEM) estimates considering the “Hidden Markov Chain” (HMC) model is quite efficient when the hidden chain is stationary. However, when the latter is not stationary, the unsupervised restoration results can be poor, due to a bad match between the real and estimated models. In this paper we present a more appropriate model for non stationary HMC, via recent Triplet Markov Chains (TMC) model. Using TMC, we show that the classical restoration results can be significantly improved in the case of non stationary data. The latter improvement is performed in an unsupervised way using a SEM parameter estimation method. Some application examples to unsupervised image segmentation are also provided.