Recent advances on graph-based image segmentation techniques

Chao Zeng, Xiangjian He, Wenjing Jia, Min Xu

Research output: Chapter in Book/Conference proceedingBook Chapterpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationImage Processing
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
PublisherIGI Global
Number of pages15
ISBN (Electronic)9781466639959
ISBN (Print)1466639946, 9781466639942
Publication statusPublished - 31 May 2013
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science (all)


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