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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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 26; číslo 9; s. 1124 - 1137
Hlavní autoři: Boykov, Y., Kolmogorov, V.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.09.2004
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Popis
Shrnutí: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.
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ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2004.60