A systematic way for region-based image segmentation based on Markov Random Field model

In this paper, we propose a Markov Random Field model-based approach as a systematic way for integrating constraints for robust image segmentation. In our approach, the image is first segmented into a set of disjoint regions by one of the region-based segmentation techniques which operates on image pixels, and a Region Adjaceny Graph (RAG) is then constructed from the resulting segmented regions based on the spatial adjacencies between regions. Our approach is then applied by defining an MRF model on the corresponding RAG. Constraints for improving the segmentation results are incorporated into an energy function via clique functions and optimal segmentation is then achieved by finding a labeling configuration which minimizes the energy function through simulated annealing.