A region Markov random field model with integrated edge feature and image segmentation algorithm

作者：Xu Shengjun; Han Jiuqiang^*; He Bo; Zhao Liang; Xiao Haiyan

来源：Journal of Xi'an Jiaotong University, 2014, 48(2): 14-19.

DOI：10.7652/xjtuxb201402003

• Belief propagation algorithm
• Edge features
• Gaussian Mixture Model
• Image segmentation algorithm
• Markov Random Field model
• Markov Random Fields
• Markov random fields models
• Natural image segmentations

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.

• 出版日期2014