Inverse Renormalization Group Transformation in Bayesian Image Segmentations

Graduate School of Informatics, Kyoto University, 36-1 Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501 Japan 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. Bayesian segmentation modeling based on Markov random fields (MRF's) is one of the interesting research topics We consider an image as defined on a set of pixels arranged on a square grid graph (V,E). HereV { i i 1, 2,···, V } denotes the set of all the pixels andE is the set of all the nearest-neighbour pairs of pixels{ i,j} . The total numbers of elements in the setsV and E are denoted by V and E, respectively.