用于图像分割的局部区域一致性流形约束MRF模型

Local region consistency manifold constrained MRF model for image segmentation

针对区域马尔可夫随机场(MRF)模型难以有效描述图像复杂先验知识的问题,提出一种基于局部区域一致性流形约束MRF(LRCMC-MRF)模型.首先,所提模型利用高维数据的低维流形分布表征图像局部区域的复杂几何结构先验,建立图像局部区域的流形先验约束;其次,基于Pairwise MRF模型,建立一种包含更多图像局部信息的局部空间自适应MRF模型;最后,基于贝叶斯理论,将复杂局部区域几何结构先验和局部空间自适应统计特征融合,利用Gibbs采样算法对所提出模型进行优化.实验结果表明,与基于常规区域的MRF模型相比,所提出的分割算法具有较好的分割效果.

Region-based Markov random fields(MRF)is usually difficult to effectively describe the prior knowledge of complex natural images.To solve this problem,a local region consistency manifold constrained MRF(RCMC-MRF)model is proposed.Firstly,the proposed model uses low-dimensional manifold distribution of high-dimensional data to characterize complex geometry structure prior in local region of images,and builds a localized manifold prior constraints term for the image segmentation model.Then,the proposed model utilizes more local region information of images to construct a local spatial adaptive MRF based on the pairwise MRF.Finally,the complex geometry structure prior and local spatial adaptive statistical feature in the local region are incorporated according to the Bayesian theory.The Gibbs sample algorithm is used for optimization.Compared with the conventional region-based MRF model,experimental result shows that the proposed model can provide a better segmentation result.