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An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
September 2004 (vol. 26 no. 9)
pp. 1124-1137
ASCII Text
x 
 
Yuri Boykov, Vladimir Kolmogorov, "An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1124-1137, September, 2004.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2004.60,
author = {Yuri Boykov and Vladimir Kolmogorov},
title = {An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {26},
number = {9},
issn = {0162-8828},
year = {2004},
pages = {1124-1137},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.60},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IS - 9
SN - 0162-8828
SP1124
EP1137
EPD - 1124-1137
A1 - Yuri Boykov,
A1 - Vladimir Kolmogorov,
PY - 2004
KW - Energy minimization
KW - graph algorithms
KW - minimum cut
KW - maximum flow
KW - image restoration
KW - segmentation
KW - stereo
KW - multicamera scene reconstruction.
VL - 26
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
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.

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Index Terms:
Energy minimization, graph algorithms, minimum cut, maximum flow, image restoration, segmentation, stereo, multicamera scene reconstruction.
Citation:
Yuri Boykov, Vladimir Kolmogorov, "An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1124-1137, Sept. 2004, doi:10.1109/TPAMI.2004.60
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