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Recent Advances on Graph-Based Image Segmentation Techniques
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Author(s): Chao Zeng (University of Technology-Sydney, Australia), Wenjing Jia (University of Technology-Sydney, Australia), Xiangjian He (University of Technology-Sydney, Australia)and Min Xu (University of Technology-Sydney, Australia)
Copyright: 2013
Pages: 15
Source title:
Graph-Based Methods in Computer Vision: Developments and Applications
Source Author(s)/Editor(s): Xiao Bai (Beihang University, China), Jian Cheng (Chinese Academy of Sciences, China)and Edwin Hancock (University of York, UK)
DOI: 10.4018/978-1-4666-1891-6.ch007
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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.
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