基于小波域分层Markov模型的纹理分割

Texture Segmentation Based on a Hierarchical Markov Model in Wavelet Domain

提出了一种新的小波域分层Markov模型。该模型使用高斯马尔可夫随机场(Gauss Markov randomfield,GMRF)模型描述每一尺度小波系数向量的分布,考虑了同一尺度特征之间的相互作用;利用尺度间的因果马尔可夫随机场(Markov random field,MRF)模型和尺度内的非因果MRF模型来描述标记场的局部作用关系,以此确定标记场的先验信息。根据贝叶斯准则,利用多目标问题优化技术,给出了此模型相应的纹理分割算法。最后,与经典模型的分割算法进行了对比实验,验证了所提出算法的有效性。

A new hierarchical Markov model in wav.elet domain was proposed. In this model, the Gauss Markov random field（GMRF） was used to model the distribution of wavelet coefficient vectors to describe the relationship of observed features on each scale, and the cooperation of interscale casual. Innnerscale non casual Markov Random Fields was exploited to model the label field priori probability. Based on the Bayesian rules, a new textured image segmentation algorithm was proposed employing multi-objective problem solving technique in this new hierarchical model. Experiments with synthetic texture images and remote sensing images were carried out. The results show the abilities of the proposed method to reduce segmentation error rate.