Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision

TL;DR

This paper comprehensively evaluates serial and parallel min-cut/max-flow algorithms in computer vision, comparing their performance on diverse problems and providing insights into their efficiency, scalability, and suitability for different scenarios.

Contribution

It offers the largest benchmark comparison of serial and parallel algorithms for min-cut/max-flow in computer vision, including new evaluation strategies and publicly available datasets.

Findings

GridCut performs best on structured graphs.

Pseudoflow algorithms achieve overall best performance.

Parallel algorithms show limited dominance, with only some scaling efficiency.

Abstract

Minimum cut/maximum flow (min-cut/max-flow) algorithms solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. As a result, it is difficult to choose an ideal algorithm for a given problem. Furthermore, parallel algorithms have not been thoroughly compared. In this paper, we evaluate the state-of-the-art serial and parallel min-cut/max-flow algorithms on the largest set of computer vision problems yet. We focus on generic algorithms, i.e., for unstructured graphs, but also compare with the specialized GridCut implementation. When applicable, GridCut performs best. Otherwise, the two pseudoflow algorithms, Hochbaum pseudoflow and excesses incremental breadth first search, achieves the overall best performance. The most memory efficient implementation tested is the Boykov-Kolmogorov algorithm. Amongst generic parallel algorithms, we find the bottom-up merging approach by Liu and Sun to be best, but no method is dominant. Of the generic parallel methods, only the parallel preflow push-relabel algorithm is able to efficiently scale with many processors across problem sizes, and no generic parallel method consistently outperforms serial algorithms. Finally, we provide and evaluate strategies for algorithm selection to obtain good expected performance. We make our dataset and implementations publicly available for further research.