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Proceedings Paper

KM_GrabCut: a fast interactive image segmentation algorithm
Author(s): Jianbo Li; Yiping Yao; Wenjie Tang
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Paper Abstract

Image segmentation is critical for image processing. Among several algorithms, GrabCut is well known by its little user interaction and desirable segmentation result. However, it needs to take a lot of time to adjust the Gaussian Mixture Model (GMM) and to cut the weighted graph with Max-Flow/Min-Cut Algorithm iteratively. To solve this problem, we first build a common algorithmic framework which can be shared by the class of GrabCut-like segmentation algorithms, and then propose KM_GrabCut algorithm based on this framework. The KM_GrabCut first uses K-means clustering algorithm to cluster pixels in foreground and background respectively, and then constructs a GMM based on each clustering result and cuts the corresponding weighted graph only once. Experimental results demonstrate that KM_GrabCut outperforms GrabCut with higher performance, comparable segmentation result and user interaction.

Paper Details

Date Published: 4 March 2015
PDF: 7 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 944313 (4 March 2015); doi: 10.1117/12.2178943
Show Author Affiliations
Jianbo Li, National Univ. of Defense Technology (China)
Yiping Yao, National Univ. of Defense Technology (China)
Wenjie Tang, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)

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