融合边缘特征的马尔可夫随机场模型及分割算法  

A Region Markov Random Field Model with Integrated Edge Feature and Image Segmentation Algorithm

作　　者：徐胜军[1,2] 韩九强[1] 何波[2] 赵亮[2] 肖海燕[2] 

机构地区：[1]西安交通大学智能网络与网络安全教育部重点实验室,西安710049 [2]西安建筑科技大学信息与控制工程学院,西安710055

出　　处：《西安交通大学学报》2014年第2期14-19,共6页Journal of Xi'an Jiaotong University

基　　金：国家自然科学基金青年基金资助项目(51209167);陕西省自然科学基金资助项目(2012JM8026;2013JM8030);陕西省教育厅专项基金资助项目(2013JK1091)

摘　　要：针对区域马尔可夫随机场(MRF)模型的图像分割中常产生边缘模糊的问题,提出了一种融合边缘特征的区域MRF模型(IEFRMRF)及其分割算法。IEFRMRF模型基于MRF理论,首先通过边缘模板提取图像的边缘特征,建立局部区域的边缘先验约束;其次利用图像局部区域像素的空间约束关系描述图像的局部高斯统计特征,并通过期望最大化算法估计高斯特征参数;然后根据贝叶斯原理建立了具有边缘保持作用的区域MRF模型;最终采用区域置信度传播(BP)算法对IEFRMRF模型进行全局优化,把局部统计特征传递到图像的全局,并按照MAP准则估计图像分割标号。人工加噪声图像分割的实验结果表明,IEFRMRF模型的分割结果和传统高斯MRF模型、局部区域高斯MRF模型的分割结果相比,分割准确率分别提高了47.9%和21.4%,并且分割结果的边缘更清晰,自然图像的分割实验也验证了提出模型的有效性。A region Markov random field model with integrated edge feature （IEFRMRF） is proposed to solve the problem that existing region Markov random fields （MRF） model often leads to produce blur edge in image segmentation. The proposed model utilizes edge templates to extract edge features of an image, and builds edge prior constraints on local regions. Space constraints among local regions of the image are used to express local Gaussian statistical features of the image, and the Gaussian parameters are estimated by maximizing expectations. Then a new local adaptive neighborhood information Gaussian mixture model （GMM） is constructed and an algorithm is proposed to estimate its parameters. The region MRF model that preserves image edge is then built based on the Bayesian theory. The region belief propagation algorithm is applied to globally optimize the IEFRMRF model. Local statistical features are transferred to the image of the global, and image segmentation labels are estimated by MAP criterion during the optimization. Experiments on an artificial noise image and comparisons with the classical Gaussian MRF model and the local region Gaussian MRF model show that the IEFRMRF model not only increases segmentation accuracy rate by 47. 9% and 21. 4%, respectively, but also acquires sharper edge of segmentation result. The validity of the proposed model is also verified by natural image segmentation experiments.

关 键 词：边缘特征 马尔可夫随机场 高斯混合模型 图像分割 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]