Inverse Renormalization Group Transformation in Bayesian Image Segmentations

TL;DR

This paper introduces a Bayesian image segmentation method that combines loopy belief propagation with inverse real space renormalization to significantly reduce computational time.

Contribution

It presents a novel integration of inverse renormalization group transformation with Bayesian segmentation, improving efficiency over traditional methods.

Findings

Computational time reduced to less than one-tenth of conventional methods.

Effective segmentation achieved with the proposed approach.

Demonstrated significant efficiency gains in experiments.

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.