Presentation 2008-03-12
Image Processing by using the EM algorithm and the belief propagation
Kei INOUE, Muneki YASUDA, Kazuyuki TANAKA,
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Abstract(in English) Markov random fields in image processing includehyperparameters to estimate from given data. We introduce a method to estimate hyperparameters by combining EM algorithm with belief propagation which is familiar computational method in the statistical learning theory. The prior probabilistic model is assumed to be the Q-Ising model and we adopt the symmetric channel and the additive white Gaussian noise as degradation process. In the method, the belief propagation procedures are stopped in a finite number of iterations. We check the influence of these hyperparameter estimations. The obtained results show that the deference between the performance of our proposed method and the one of the conventional EM algorithm with belief propagation is very small even in the case where the number of iterations in the belief propagation is restricted to one.
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Keyword(in English) Probabilistic information processing / Statistical learning / Markov random field / EM algorithm / Belief propagation
Paper # NC2007-118
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Committee NC
Conference Date 2008/3/5(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Image Processing by using the EM algorithm and the belief propagation
Sub Title (in English)
Keyword(1) Probabilistic information processing
Keyword(2) Statistical learning
Keyword(3) Markov random field
Keyword(4) EM algorithm
Keyword(5) Belief propagation
1st Author's Name Kei INOUE
1st Author's Affiliation Graduate School of Information Sciences, Tohoku University()
2nd Author's Name Muneki YASUDA
2nd Author's Affiliation Graduate School of Information Sciences, Tohoku University
3rd Author's Name Kazuyuki TANAKA
3rd Author's Affiliation Graduate School of Information Sciences, Tohoku University
Date 2008-03-12
Paper # NC2007-118
Volume (vol) vol.107
Number (no) 542
Page pp.pp.-
#Pages 6
Date of Issue