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.
Conference Title
Proceedings of the 13th International Conference on Machine Learning and Cybernetics
Publisher URI
Copyright Statement
Copyright 2014 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.c
Subject
Computer Vision