MRF model and FRAME model-based unsupervised image segmentation  

作　　者：CHENGBing WANGYing ZHENGNanning JIAXinchun 

机构地区：[1]InstituteofArtificialIntelligenceandRobotics,Xi'anJiaotongUniversity,Xi'an710049,China//DepartmentofBiomedicalEngineering,Xi'anJiaotongUniversity,Xi'an710049,China [2]TheResearchCenterofTheFirstHospital,Xi'anJiaotongUniversity,Xi'an710061,China [3]InstituteofArtificialIntelligenceandRobotics,Xi'anJiaotongUniversity,Xi'an710049,China

出　　处：《Science in China(Series F)》2004年第6期697-705,共9页中国科学（F辑英文版）

摘　　要：This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses 'Filters, Random and Maximum Entropy (Abb. FRAME)' model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an experiment with synthesized and real images is given, which shows that the method can segment images with complex textures efficiently and is robust to noise.This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses 'Filters, Random and Maximum Entropy (Abb. FRAME)' model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an experiment with synthesized and real images is given, which shows that the method can segment images with complex textures efficiently and is robust to noise.

关 键 词：image segmentation Markov random field FRAME model Maximum a Posterior estimation iterated conditional modes. 

分 类 号：TN911.73[电子电信—通信与信息系统]