Unsupervised Image Segmentation Based on Bethe Approximation
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概要
- 論文の詳細を見る
We propose an approach for unsupervised image segmentation based on the Markov random field by using the Bethe approximation. We first derive the Bayesian information criterion under the Bethe approximation and then propose an iterative algorithm to search a model which fits the image data best. For this aim, we derive a criterion for merging two components among several components in terms of a perturbation expansion. Namely, annihilation of components is implemented by merging two components into one component after each convergence of the supervised segmentation with a fixed number of components. We find by numerical experiments that the optimal number of components is selected from the series of local optima with different numbers of components and the best result for segmentation is obtained with good performance.
- 東北大学の論文
著者
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Chen Fan
Department Of Biological Sciences National University Of Singapore
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Aoki Takafumi
Department Of Computer And Mathematical Sciences Graduate School Of Information Sciences Tohoku Univ
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Aoki Takafumi
Department Of Chemistry Graduate School Of Science Osaka University
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Horiguchi Tsuyoshi
Department Of Applied Science Faculty Of Engineering Tohoku University
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Horiguchi Tsuyoshi
Department Of Computer And Mathematical Sciences Graduate School Of Information Sciences Tohoku Univ
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Chen Fan
Department Of Computer And Mathematical Sciences Graduate School Of Information Sciences Tohoku Univ
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