A hybrid approach toward model-based texture segmentation

The authors develop a hybrid texture segmentation algorithm, combining statistical (maximum likelihood/maximum a posteriori) and structural (local consensus) classification techniques. This algorithm exhibits several advantages over traditional algorithms based solely on statistical models; (i) homogeneity and integrity of each region in the texture mosaic can be included implicitly by a local voting scheme, in addition to explicit modeling through Gibbs random fields, and (ii) modified maximum likelihood solution of the hybrid segmentation algorithm provides a better initial estimate of region boundaries which can be used in a maximum a posteriori segmentation algorithm. With this initial estimate, an iterative, semideterministic relaxation algorithm, called mean field annealing, is used to locate the nearly global optimum solution efficiently.<
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