Parallel and Distributed Graph Cuts by Dual Decomposition

Graph cuts methods are at the core of many state-of-theart algorithms in computer vision due to their efficiency in computing globally optimal solutions. In this paper, we solve the maximum flow/minimum cut problem in parallel by splitting the graph into multiple parts and hence, further increase the computational efficacy of graph cuts. Optimality of the solution is guaranteed by dual decomposition, or more specifically, the solutions to the subproblems are constrained to be equal on the overlap with dual variables. We demonstrate that our approach both allows (i) faster processing on multi-core computers and (ii) the capability to handle larger problems by splitting the graph across multiple computers on a distributed network. Even though our approach does not give a theoretical guarantee of speedup, an extensive empirical evaluation on several applications with many different data sets consistently shows good performance. An open source implementation of the dual decomposition meth...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Petter Strandmark, Fredrik Kahl
• Keywords: Computer Vision, CVPR 2010, Dual Decomposition, Graph Cuts, Graph Cuts Methods