An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision

Minimum cut/maximum flow algorithms on graphs have emerged as an increasingly useful tool for exactor approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical ef...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 26; H. 9; S. 1124 - 1137
Hauptverfasser:	Boykov, Y., Kolmogorov, V.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2004 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Application software Artificial Intelligence Cluster Analysis Clustering algorithms Computer vision Energy Transfer graph algorithms Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image restoration Image segmentation Imaging, Three-Dimensional - methods Index Terms- Energy minimization Information Storage and Retrieval - methods Iterative algorithms Labeling maximum flow Minimization methods minimum cut multicamera scene reconstruction Pattern Recognition, Automated - methods Photogrammetry - methods Photography - methods Reproducibility of Results segmentation Sensitivity and Specificity Simulated annealing stereo Stereo vision Studies
ISSN:	0162-8828, 1939-3539
Online-Zugang:	Volltext
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Abstract	Minimum cut/maximum flow algorithms on graphs have emerged as an increasingly useful tool for exactor approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push -relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.
AbstractList	The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Minimum cut/maximum flow algorithms on graphs have emerged as an increasingly useful tool for exactor approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push -relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes. After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes. After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.
Author	Kolmogorov, V. Boykov, Y.
Author_xml	– sequence: 1 givenname: Y. surname: Boykov fullname: Boykov, Y. organization: Dept. of Comput. Sci., Univ. of Western Ontario, London, Ont., Canada – sequence: 2 givenname: V. surname: Kolmogorov fullname: Kolmogorov, V.
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/15742889$$D View this record in MEDLINE/PubMed
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Snippet	Minimum cut/maximum flow algorithms on graphs have emerged as an increasingly useful tool for exactor approximate energy minimization in low-level vision. The... After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy... The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity.
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SubjectTerms	Algorithms Application software Artificial Intelligence Cluster Analysis Clustering algorithms Computer vision Energy Transfer graph algorithms Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image restoration Image segmentation Imaging, Three-Dimensional - methods Index Terms- Energy minimization Information Storage and Retrieval - methods Iterative algorithms Labeling maximum flow Minimization methods minimum cut multicamera scene reconstruction Pattern Recognition, Automated - methods Photogrammetry - methods Photography - methods Reproducibility of Results segmentation Sensitivity and Specificity Simulated annealing stereo Stereo vision Studies
Title	An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
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