Interactive Organ Segmentation Using Graph Cuts
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Object segmentation using graph cuts based active contours
Computer Vision and Image Understanding
A New Image Segmentation Method Based on Grey Graph Cut
CSO '10 Proceedings of the 2010 Third International Joint Conference on Computational Science and Optimization - Volume 01
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper we propose an automatic salient object extraction method for nature scene. The proposed method first utilizes an algorithm based on visual attention model to obtain a prior knowledge for Graph Cut, and then constructs the weighted graph of Graph Cut based on super-pixels pre-segmented by the improved watershed algorithm in order to accelerate the speed of proposed method. In this framework, Visual saliency map is obtained using chrominance and intensity features in HSV color space, which provides the approximate region that contains salient object to be segmented. Then the salient object region after extension is cropped as input image, and pre-segmented by the improved watershed algorithm into several regions to construct weighted graph. Finally the salient object is obtained by Graph Cut algorithm. Experiment results show that our algorithm can automatically get salient object without human interactions, and speed up the segmentation without decreasing segmentation accuracy.