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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhang, L., Ji, Q.: Image segmentation with a unified graphical model. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 1406–1425 (2010)
Boykov, Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. In: Proceedings of Eighth IEEE International Conference on Computer Vision ICCV 2001, pp. 105–112 (2001)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient ND image segmentation. International Journal of Computer Vision 70, 109–131 (2006)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1222–1239 (2001)
Goldberg, A.V., Tarjan, R.E.: A new approach to the maximum-flow problem. Journal of the ACM (JACM) 35, 921–940 (1988)
Delong, A., Boykov, Y.: Globally optimal segmentation of multi-region objects. In: IEEE 12th International Conference on Computer Vision, pp. 285–292 (2009)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)
Delong, A., Boykov, Y.: A scalable graph-cut algorithm for ND grids. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR, pp. 1–8 (2008)
Zhang, L., Zeng, Z., Ji, Q.: Probabilistic image modeling with an extended chain graph for human activity recognition and image segmentation. IEEE Transactions on Image Processing 20, 2401–2413 (2011)
Hinton, G.E., Osindero, S., Bao, K.: Learning causally linked markov random fields. In: AI & Statistics (2005)
Regazzoni, C., Murino, V., Vernazza, G.: Distributed propagation of a-priori constraints in a Bayesian network of Markov random fields. IEE Proceedings I (Communications, Speech and Vision) 140, 46–55 (1993)
Liu, F., Xu, D., Yuan, C., Kerwin, W.: Image segmentation based on Bayesian network-Markov random field model and its application to in vivo plaque composition. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 141–144 (2006)
Borenstein, E., Ullman, S.: Learning to Segment. In: Proc. European Conf. Computer Vision, pp. 1–8 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Beheshti, M., Liew, A.WC. (2014). Image Segmentation Based on Graph-Cut Models and Probabilistic Graphical Models: A Comparative Study. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_37
Download citation
DOI: https://doi.org/10.1007/978-3-662-45652-1_37
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45651-4
Online ISBN: 978-3-662-45652-1
eBook Packages: Computer ScienceComputer Science (R0)