4 March 2015 KM_GrabCut: a fast interactive image segmentation algorithm
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Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 944313 (2015) https://doi.org/10.1117/12.2178943
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China
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.
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Jianbo Li, Jianbo Li, Yiping Yao, Yiping Yao, Wenjie Tang, Wenjie Tang, } "KM_GrabCut: a fast interactive image segmentation algorithm", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 944313 (4 March 2015); doi: 10.1117/12.2178943; https://doi.org/10.1117/12.2178943
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