SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Detecting and extracting the photo composites using planar homography and graph cut
IEEE Transactions on Information Forensics and Security
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In this paper we propose a novel approach to the problem of interactive foreground/background segmentation in images. With user provided strokes which indicate foreground and background seeds, we estimate two Gaussian mixture models, one for foreground and the other for background, and define two quantities to measure the initial probabilities of each pixel belonging to the foreground and the background respectively. An optimization function constructed based on the quantities and the boundary and coherent region information is proposed to solve the segmentation problem. By relaxing the hard binary segmentation to a soft labelling problem in the continuous domain, a closed form global optimal solution can be achieved, which directly results in the final binary segmentation output. Experimental results demonstrate the excellent performance of our algorithm.