A Region-determined Hierarchical Markov Model and Its MPM Algorithm
基于区域确定的分层马尔可夫模型及其MPM算法

The noniterative algorithm of discrete hierarchical Markov random field(MRF)model has much lower com- puting complexity and better result than its iterative counterpart of noncausal MRF model,since it has causality property between layers.However,traditional hierarchical MRF model always results in the block artifacts and discontinuous edges. In this paper,a new region-determined half tree hierarchical MRF model is proposed and its region-determined maximizer of the posteriori marginals(MPM)algorithm is inferred. Based on over-segmentation of the watershed algorithm,the proposed model converts pixel probabilities between layers into region probabilities and obtains the final segmentation. The experiments on supervised SAR image segmentation demonstrate that the proposed method performs better than the pixel-based hierarchical model as well as the Gibbs sampler with the single resolution model.