Image Segmentation via Mean Shift and Loopy Belief Propagation  

作　　者：JIA Jianhua,JIAO Licheng,CHANG Xia Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China/Institute of Intelligent Information Processing,Xidian University,Xi’an 710071,Shaanxi,China 

出　　处：《Wuhan University Journal of Natural Sciences》2010年第1期43-50,共8页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Natural Science Foundation of China (60703107, 60703108);the National High Technology Research and Development Program of China (863 Program) (2006AA01Z107);the National Basic Research Program of China (973 Program) (2006CB705700);the Program for Changjiang Scholars and Innovative Research Team in University (IRT0645)

摘　　要：This paper presents a novel approach that can quickly and effectively partition images based on fully exploiting the spatially coherent property. We propose an algorithm named iterative loopy belief propagation（iLBP） to integrate the homogenous regions and prove its convergence. The image is first segmented by mean shift（MS） algorithm to form over-segmented regions that preserve the desirable edges and spatially coherent parts. The segmented regions are then represented by region adjacent graph（RAG） . Motivated by k-means algorithm,the iLBP algorithm is applied to perform the minimization of the cost function to integrate the over-segmented parts to get the final segmentation result. The image clustering based on the segmented regions instead of the image pixels reduces the number of basic image entities and enhances the image segmentation quality. Comparing the segmentation result with some existing algorithms,the proposed algorithm shows a better performance based on the evaluation criteria of entropy especially on complex scene images.This paper presents a novel approach that can quickly and effectively partition images based on fully exploiting the spatially coherent property. We propose an algorithm named iterative loopy belief propagation（iLBP） to integrate the homogenous regions and prove its convergence. The image is first segmented by mean shift（MS） algorithm to form over-segmented regions that preserve the desirable edges and spatially coherent parts. The segmented regions are then represented by region adjacent graph（RAG） . Motivated by k-means algorithm,the iLBP algorithm is applied to perform the minimization of the cost function to integrate the over-segmented parts to get the final segmentation result. The image clustering based on the segmented regions instead of the image pixels reduces the number of basic image entities and enhances the image segmentation quality. Comparing the segmentation result with some existing algorithms,the proposed algorithm shows a better performance based on the evaluation criteria of entropy especially on complex scene images.

关 键 词：image segmentation mean shift loopy belief propagation spatial property 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] O224[自动化与计算机技术—计算机科学与技术]