Unsupervised image segmentation algorithm based on HMRF model

This paper presents an unsupervised image segmentation algorithm based on hidden Markov random field (HMRF) model. For each order model segmentation the proposed algorithm makes use of the correlated information between adjacent models. Therefore the algorithm avoids the drawback about that mean field algorithm is restricted by initial condition. Furthermore, in order to solve the model selection problems of unsupervised image segmentation, the sum of squared error criterion with penalty term is proposed. The experiment results testify that the proposed criterion is superior to the Pseudo-likelihood Information criterion (PLIC), and it is shown that the performance of the segmentation is satisfied.