Abstract-Markov random fields are used extensively in modelbased approaches to image segmentation and, under the Bayesian paradigm, are implemented through Markov chain Monte Carlo (MCMC) methods. In this paper, we describe a class of such models (the double Markov random field) for images composed of several textures, which we consider to be the natural hierarchical model for such a task. We show how several of the Bayesian approaches in the literature can be viewed as modifications of this model, made in order to make MCMC implementation possible. From a simulation study, conclusions are made concerning the performance of these modified models. Index Terms-Bayesian statistics, hierarchical model, image segmentation, Markov random field, remote sensing.
A double Markov Random Fields (DMRF) model is exploited REF in order to overcome this limitation.
Double Markov Random Fields and Bayesian Image Segmentation
ICIP (1)
Computer Science