Texture image segmentation using hierarchical MRF model based on the interactive potential function and mean-field parameter estimation

被引：0

作者：

Li, Yi-Bing ^{[1
]}

Yang, Peng ^{[1
]}

Ye, Fang ^{[1
]}

Liu, Dan-Dan ^{[2
]}

机构：

[1] College of Information and Communication Engineering, Harbin Engineering University, Harbin

[2] College of Electrical and Control Engineering, Heilongjiang University of Science and Technology, Harbin

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2015年 / 45卷 / 06期

关键词：

Communication; Hierarchical MRF; Image segmentation; Interactive potential function; Linear equation; Mean-field theory;

D O I：

10.13229/j.cnki.jdxbgxb201506049

中图分类号：

学科分类号：

摘要：

To solve the difficult problem of expectations caused by interaction between hidden variables when using Expectation-maximization (EM) algorithm to estimate parameters for hierarchical Markov Random Fields (MRF) model, the mean-field theory is introduced into Gaussian-MRF (GMRF) model. Parameters can be estimated easily through simple linear equation in case of without window function. An interactive potential function based on Bayesian belief propagation algorithm is proposed to change the situation that the fixed or variable weighted potential function can not express the interaction of image regions. Experiments demonstrate that the proposed method not only has good regional classification but also smoothly internal region. In addition, the mixed and confused phenomenon of traditional hierarchical MRF is improved in wavelet domain. ©, 2015, Editorial Board of Jilin University. All right reserved.

引用

页码：2075 / 2079

页数：4

共 9 条

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