Bayesian image modeling by means of a generalized sparse prior and loopy belief propagation

Kazuyuki Tanaka, Muneki Yasuda, D. Michael Titterington

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Bayesian image modeling is presented based on a generalized sparse prior probability distribution. Our prior includes sparsity in each interaction term between every pair of neighbouring pixels in Markov random fields. A new scheme for hyperparameter estimation is based on the conditional maximization of entropy in our generalized sparse prior. In addition, the criterion used for defining the optimal value for sparseness in interactions is that of the maximization of marginal likelihood. Our practical algorithm is based on loopy belief propagation.

Original languageEnglish
Article number114802
Journaljournal of the physical society of japan
Volume81
Issue number11
DOIs
Publication statusPublished - 2012 Nov

Keywords

  • Bayesian statistics
  • Belief propagation
  • Markov random fields
  • Maximum likelihood estimation
  • Probabilistic image processing
  • Statistical-mechanical informatics

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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