Inverse renormalization group transformation in Bayesian image segmentations

Kazuyuki Tanaka, Shun Kataoka, Muneki Yasuda, Masayuki Ohzeki

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


A new Bayesian image segmentation algorithm is proposed by combining a loopy belief propagation with an inverse real space renormalization group transformation to reduce the computational time. In results of our experiment, we observe that the proposed method can reduce the computational time to less than one-tenth of that taken by conventional Bayesian approaches.

Original languageEnglish
Pages (from-to)45001
Number of pages1
JournalJournal of the Physical Society of Japan
Issue number4
Publication statusPublished - 2015 Apr 15


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