Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Quantitative Evaluation of a Novel Image Segmentation Algorithm
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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We propose a fully automated variation of the GrabCut technique for segmenting comparatively simple images with little variation in background colour and relatively high contrast between foreground and background. The interactive trimap generation central to the original formulation of GrabCut is replaced by a tentative approximation of the background using active contours. Instead of waiting until convergence of the iterated graph cut, we terminate as soon as the Gaussian models of foreground and background are (locally) maximally separated. We demonstrate that this results in equivalent segmentation quality at significantly lower cost. A comparison with three alternative segmentation techniques, including normalised cut, indicates that themethod is eminently suitable for the chosen image domain.