Image Segmentation Based on Graph-Cut Models and Probabilistic Graphical Models: A Comparative Study

Maedeh Beheshti, Alan Wee-Chung Liew

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

External IDs:dblp:conf/icmlc/BeheshtiL14