Image Segmentation based on Graph-Cut Models and Probabilistic Graphical Models: a Comparative Study

Abstract

Image Segmentation has been one of the most important unsolved problems in computer vision for many years. Recently, there have been great efforts in producing better segmentation algorithms. The purpose of this paper is to introduce two recently proposed graph based segmentation methods, namely, graph-cut models (deterministic) and unified graphical model (probabilistic). We present some foreground/background segmentation results to illustrate their performance on images with complex background scene.