Self-organised parameter estimation and segmentation of MRF model-based texture images

A combination of the self-organising principle and a relaxation method is proposed for the unsupervised segmentation of textured images. There are two phases to this segregation. The first one is region-based and provides a coarse rapid segmentation. A hierarchical self-organising learning structure together with a Markov random field (MRF) model-based LS estimator is used for parameter estimation and classification of different regions from a randomly located window, which shrinks with time. A globally correct separation of different regions has been always formed during extensive experiments. These results are used as the initial states for the second phase processing, which is boundary-based. A relaxation method, similar to the Metropolis algorithm, is used for boundary improvement. Over a small area of original boundary, a new boundary is randomly generated, which accepted or rejected according to the local energy change. Experiment results on both synthetic and real textured images are presented