Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm

TL;DR

This paper introduces the fcBK algorithm, a faster and more memory-efficient method for computing minimum s-t cuts, capable of handling extremely large graphs with improved theoretical complexity and practical performance.

Contribution

The paper presents the fcBK algorithm with a new compact graph representation, achieving linear time complexity in the capacity of the cut and enabling large-scale graph processing.

Findings

The fcBK algorithm has a time complexity of O(m|C|).

The implementation can process graphs with over 10^9 vertices and 10^10 edges.

The implementation is the fastest among existing BK algorithms on benchmark datasets.

Abstract

Computing a minimum $s$-$t$ cut in a graph is a solution to a wide range of computer vision problems, and is often done using the Boykov-Kolmogorov (BK) algorithm. In this paper, we revisit the BK algorithm from both a theoretical and practical point of view. We improve the analysis of the time complexity of the BK algorithm to $O(mn|C|)$ and propose a new algorithm, the fast and compact BK (fcBK) algorithm, with a time complexity of $O(m|C|)$, where $m$, $n$, and $|C|$ are the number of edges, number of vertices, and the capacity of the cut, respectively. We additionally propose a compact graph representation that allows our implementation to find a minimum $s$-$t$ cut in a graph with upwards of $10^9$ vertices and $10^{10}$ edges on a machine with 128 GB of memory. We find our implementation of the BK algorithm to be the fastest available implementation of the BK algorithm when evaluating on a comprehensive set of benchmark datasets, highlighting the importance of memory-efficient implementations. We make our implementations publicly available for further research and implementation development within minimum $s$-$t$ cut algorithms.