An Efficient Image Segmentation Algorithm Based on Bayes Decision Theory and its Application for Region Detection

This study aims to identify rectangular regions where the features follow an independent and identically distributed probability. To achieve this, we employ a method that divides large regions into smaller ones. We model the image using a quadtree structure and assume prior distributions for the image, labels, and quadtree. Using Bayesian decision theory, we derive the optimal estimation solution. Few studies have considered prior distributions on images for optimal estimation. We reduced computational complexity by selecting an appropriate prior distribution for the quadtree. Additionally, we estimated the parameters of each probability distribution using the training data.