Competitive Analysis of Minimum-Cut Maximum Flow Algorithms in Vision Problems
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Rapid advances in image acquisition and storage technology underline the need for algorithms that are capable of solving large scale image processing and computer-vision problems. The minimum cut problem plays an important role in processing many of these imaging problems such as, image and video segmentation, stereo vision, multi-view reconstruction and surface fitting. While several min-cut/max-flow algorithms can be found in the literature, their performance in practice has been studied primarily outside the scope of computer vision. We present here the results of a comprehensive computational study, in terms of execution times and memory utilization, of four recently published algorithms, which optimally solve the s-t cut and maximum flow problems: (i) Goldberg's and Tarjan's Push-Relabel; (ii) Hochbaum's pseudoflow; (iii) Boykov's and Kolmogorov's augmenting paths; and (iv) Goldberg's partial augment-relabel. Our results demonstrate that the Hochbaum's pseudoflow algorithm, is faster and utilizes less memory than the other algorithms on all problem instances investigated.
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