Image Segmentation Based on Bethe Approximation for Gaussian Mixture Model

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概要

• 論文の詳細を見る

• 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.

• 東北大学の論文

著者

• Tanaka Kazuyuki

  Dep. Of Applied Information Sciences Graduate School Of Information Sciences Tohoku Univ.

• CHEN Fan

  Guraduate School of Information Sciences, Department of Computer and Mathematical Science, Tohoku Un

• TANAKA Kazuyuki

  GSIS, Department of Computer and Mathematical Science, Tohoku University

• HORIGUCHI Tsuyoshi

  GSIS, Department of Computer and Mathematical Science, Tohoku University

• Chen Fan

  Guraduate School Of Information Sciences Department Of Computer And Mathematical Science Tohoku Univ

• Horiguchi Tsuyoshi

  Gsis Department Of Computer And Mathematical Science Tohoku University

関連論文

• Image Segmentation Based on Bethe Approximation for Gaussian Mixture Model
• Solvable Probabilistic Model for Cycles in Planar Graph

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