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Rapid advances in image acquisition and storage technology underline the needfor algorithms that are capable of solving large scale image processing andcomputer-vision problems. The minimum cut problem plays an important role inprocessing many of these imaging problems such as, image and videosegmentation, stereo vision, multi-view reconstruction and surface fitting.While several min-cut/max-flow algorithms can be found in the literature, theirperformance in practice has been studied primarily outside the scope ofcomputer vision. We present here the results of a comprehensive computationalstudy, in terms of execution times and memory utilization, of four recentlypublished algorithms, which optimally solve the {\em s-t} cut and maximum flowproblems: (i) Goldberg's and Tarjan's {\em Push-Relabel}; (ii) Hochbaum's {\empseudoflow}; (iii) Boykov's and Kolmogorov's {\em augmenting paths}; and (iv)Goldberg's {\em partial augment-relabel}. Our results demonstrate that the {\emHochbaum's pseudoflow} algorithm, is faster and utilizes less memory than theother algorithms on all problem instances investigated.

Competitive Analysis of Minimum-Cut Maximum Flow Algorithms in Vision Problems