最大流/最小割算法的简介，理解常用最大流最小割概念的文献，值得学习。
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.
