Inverse Renormalization Group Transformation in Bayesian Image Segmentations
Computer Vision and Pattern Recognition
2015-03-16 v1 Statistical Mechanics
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1501.00834,
title = {Inverse Renormalization Group Transformation in Bayesian Image Segmentations},
author = {Kazuyuki Tanaka and Shun Kataoka and Muneki Yasuda and Masayuki Ohzeki},
journal= {arXiv preprint arXiv:1501.00834},
year = {2015}
}
Comments
6 pages, 2 figures