视觉问题中最小割最大流算法的竞争分析
Competitive Analysis of Minimum-Cut Maximum Flow Algorithms in Vision Problems
摘要：
图像采集和存储技术的快速发展强调了对能够解决大规模图像处理和计算机视觉问题的算法的需求。最小割问题在图像和视频分割、立体视觉、多视点重建和曲面拟合等成像问题中起着重要的作用。虽然在文献中可以找到几种最小割/最大流算法，但它们在实践中的性能主要是在计算机视觉的范围之外进行研究的。本文从执行时间和内存利用率两个方面对最近发表的四种最优求解{ \em s-t}割和最大流问题的算法进行了全面的计算研究：（i）Goldberg和Tarjan的{ \em Push-Relabel};（ii）Hochbaum的{ \em pseudoflow};（iii）Boykov和Kolmogorov的{ \em增广路径};和（iv）Goldberg的{ \em部分扩增-重新标记}。我们的研究结果表明，{ \em Hochbaum的pseudoflow}算法，是更快，占用更少的内存比其他算法的所有问题的情况下调查。
英文摘要：
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 {\em s-t} cut and maximum flow problems: (i) Goldberg's and Tarjan's {\em Push-Relabel}; (ii) Hochbaum's {\em pseudoflow}; (iii) Boykov's and Kolmogorov's {\em augmenting paths}; and (iv) Goldberg's {\em partial augment-relabel}. Our results demonstrate that the {\em Hochbaum's pseudoflow} algorithm, is faster and utilizes less memory than the other algorithms on all problem instances investigated.
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