文章基本信息

标题：Image Segmentation Based on Bethe Approximation for Gaussian Mixture Model
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作者：Fan CHEN ; Kazuyuki TANAKA ; Tsuyoshi HORIGUCHI 等
期刊名称：Interdisciplinary Information Sciences
印刷版ISSN：1340-9050
电子版ISSN：1347-6157
出版年度：2005
卷号：11
期号：1
页码：17-29
DOI：10.4036/iis.2005.17
出版社：The Editorial Committee of the Interdisciplinary Information Sciences
摘要：We propose an image segmentation algorithm under an expectation-maximum scheme using a Bethe approximation. In the stochastic image processing, the image data is usually modeled in terms of Markov random fields, which can be characterized by a Gibbs distribution. The Bethe approximation, which takes account of nearest-neighbor correlations, provides us with a better approximation to the Gibbs free energy than the commonly used mean-field approximation. We apply the Bethe approximation to the image segmentation problem and investigate its efficiency by numerical experiments. As a result, our approach shows better robustness and faster converging speed than those using the mean-field approximation.
关键词：image segmentation;Bethe approximation;EM algorithm;Gaussian mixture model