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Mathematical Programming: Series A and B
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Journal of the ACM (JACM)
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STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
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SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
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ACM SIGMETRICS Performance Evaluation Review
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilevel Banded Graph Cuts Method for Fast Image Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Cuts via $\ell_1$ Norm Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Discrete and Continuous Shape Optimization Methods
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Uncertainty driven multi-scale optimization
Proceedings of the 32nd DAGM conference on Pattern recognition
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MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Efficient pixel-grouping based on dempster's theory of evidence for image segmentation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Learning object appearance from occlusions using structure and motion recovery
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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This paper proposes an algorithm for image segmentation using GraphCuts which can be used to efficiently solve labeling problems on high resolution images or resource-limited systems. The basic idea of the proposed algorithm is to simplify the original graph while maintaining the maximum flow properties. The resulting Slim Graph can be solved with standard maximum flow/minimum cut-algorithms. We prove that the maximum flow/minimum cut of the Slim Graph corresponds to the maximum flow/minimum cut of the original graph. Experiments on image segmentation show that using our graph simplification leads to significant speedup and memory reduction of the labeling problem. Thus large-scale labeling problems can be solved in an efficient manner even on resource-limited systems.