Parallel Graph-Cuts by Adaptive Bottom-up Merging

Abstract

Graph-cuts optimization is prevalent in vision and graphics problems. It is thus of great practical importance to parallelize the graph-cuts optimization using today's ubiquitous multi-core machines. However, the current best serial algorithm by Boykov and Kolmogorov (called the BK algorithm) still has the superior empirical performance. It is non-trivial to parallelize as expensive synchronization overhead easily offsets the advantage of parallelism. In this paper, we propose a novel adaptive bottom-up approach to parallelize the BK algorithm. We first uniformly partition the graph into a number of regularly-shaped disjoint subgraphs and process them in parallel, then we incrementally merge the subgraphs in an adaptive way to obtain the global optimum. The new algorithm has three benefits: 1) it is more cache-friendly within smaller subgraphs; 2) it keeps balanced workloads among computing cores; 3) it causes little overhead and is adaptable to the number of available cores. Extensive experiments in common applications such as 2D/3D image segmentations and 3D surface fitting demonstrate the effectiveness of our approach.