Unsupervised image segmentation by Global and local Criteria Optimization Based on Bayesian Networks

TL;DR

This paper introduces an unsupervised image segmentation method that models segmentation quality using Bayesian networks and combines local and global criteria optimization with approximate inference algorithms.

Contribution

It proposes a novel Bayesian network-based approach for image segmentation that integrates local evaluation measures and combines two inference algorithms for improved performance.

Findings

Effective segmentation quality modeling with Bayesian networks

Combining ICM and max-product algorithms enhances segmentation results

Approach achieves competitive performance in unsupervised image segmentation

Abstract

Today Bayesian networks are more used in many areas of decision support and image processing. In this way, our proposed approach uses Bayesian Network to modelize the segmented image quality. This quality is calculated on a set of attributes that represent local evaluation measures. The idea is to have these local levels chosen in a way to be intersected into them to keep the overall appearance of segmentation. The approach operates in two phases: the first phase is to make an over-segmentation which gives superpixels card. In the second phase, we model the superpixels by a Bayesian Network. To find the segmented image with the best overall quality we used two approximate inference methods, the first using ICM algorithm which is widely used in Markov Models and a second is a recursive method called algorithm of model decomposition based on max-product algorithm which is very popular in the recent works of image segmentation. For our model, we have shown that the composition of these two algorithms leads to good segmentation performance.