Recent Advances on Graph-Based Image Segmentation Techniques

Recent Advances on Graph-Based Image Segmentation Techniques

Chao Zeng, Wenjing Jia, Xiangjian He, Min Xu
ISBN13: 9781466618916|ISBN10: 1466618914|EISBN13: 9781466618923
DOI: 10.4018/978-1-4666-1891-6.ch007
Cite Chapter Cite Chapter

MLA

Zeng, Chao, et al. "Recent Advances on Graph-Based Image Segmentation Techniques." Graph-Based Methods in Computer Vision: Developments and Applications, edited by Xiao Bai, et al., IGI Global, 2013, pp. 140-154. https://doi.org/10.4018/978-1-4666-1891-6.ch007

APA

Zeng, C., Jia, W., He, X., & Xu, M. (2013). Recent Advances on Graph-Based Image Segmentation Techniques. In X. Bai, J. Cheng, & E. Hancock (Eds.), Graph-Based Methods in Computer Vision: Developments and Applications (pp. 140-154). IGI Global. https://doi.org/10.4018/978-1-4666-1891-6.ch007

Chicago

Zeng, Chao, et al. "Recent Advances on Graph-Based Image Segmentation Techniques." In Graph-Based Methods in Computer Vision: Developments and Applications, edited by Xiao Bai, Jian Cheng, and Edwin Hancock, 140-154. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-1891-6.ch007

Export Reference

Mendeley
Favorite

Abstract

Image segmentation techniques using graph theory has become a thriving research area in computer vision community in recent years. This chapter mainly focuses on the most up-to-date research achievements in graph-based image segmentation published in top journals and conferences in computer vision community. The representative graph-based image segmentation methods included in this chapter are classified into six categories: minimum-cut/maximum-flow model (called graph-cut in some literatures), random walk model, minimum spanning tree model, normalized cut model and isoperimetric graph partitioning. The basic rationales of these models are presented, and the image segmentation methods based on these graph-based models are discussed as the main concern of this chapter. Several performance evaluation methods for image segmentation are given. Some public databases for testing image segmentation algorithms are introduced and the future work on graph-based image segmentation is discussed at the end of this chapter.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.