Image segmentation and parameters estimation based on fuzzy Markov random field with possibility theory

Fuzzy Markov random field (FMRF) is an efficient model for data clustering or image segmentation. An improved FMRF segmentation method based on the possibility theory is presented. The originality of this algorithm based on the fact that pairwise fuzzy model is incapacious to describe the correlations between different classes in mulu-level segmentation.Possibility theory is just a shortcut for this kind of multi -level uncertain classification, which could give more flexible searching ability. The possibility theory is introduced into FMRF to strengthen abilities for searching optimum and the potential energy definitions for similar cliques is proposed. Firstly, definitions about possibility theory in improved FMRF are given. Then, EM algorithms are used to estimate unknown parameters. Finally the experiments demonstrate that the algorithm is efficient to distinguish fuzzy edges or mixed areas.