A Hierarchical Markov Image Model and Its Inference Algorithm
一种分层马尔可夫图像模型及其推导算法

The noniterative algorithm of discrete hierarchical MRF (Markov random field) model has much lower computing complexity and better result than its iterative counterpart of noncausal MRF model, since it has causality property between layers. A new model based on the hierarchical MRFhalf tree model is proposed for only one image can be obtained in image segmentation, whose MPM (maximizer of the posterior marginals) algorithm is inferred too. The proposed model not only inherits the advantages of general hierarchical MRF model but also does better: it makes large image more tractable within much less time, prevents data underflow appeared in computing, and alleviates the block artifacts occurred in hierarchical models. It is especially fit for large scale images.