A segmentation method for textured images based on the maximum posterior mode criterion

We consider the problem of semi-supervised segmentation of textured images. Recently, reweighted belief propagation has been introduced as a solution for Bayesian inference with respect to the maximum posterior mode criterion. In this pa per, we show how to adapt reweighted belief propagation to the problem of segmentation of textured images. An adaptive parameter estimation technique is also provided. Then, we compare classical simulated annealing with the recently introduced reweighted belief propagation algorithm, in terms of segmentation results.