SlimCuts: GraphCuts for High Resolution Images Using Graph Reduction

Abstract. This paper proposes an algorithm for image segmentation using GraphCuts which can be used to efficiently solve labeling problems on high resolution images or resource-limited systems. The basic idea of the proposed algorithm is to simplify the original graph while maintaining the maximum flow properties. The resulting Slim Graph can be solved with standard maximum flow/minimum cut-algorithms. We prove that the maximum flow/minimum cut of the Slim Graph corresponds to the maximum flow/minimum cut of the original graph. Experiments on image segmentation show that using our graph simplification leads to significant speedup and memory reduction of the labeling problem. Thus large-scale labeling problems can be solved in an efficient manner even on resource-limited systems.

• Type: Journal
• Year: 2011
• Where: EMMCVPR
• Authors: Björn Scheuermann, Bodo Rosenhahn
• Keywords: Computer Vision, EMMCVPR 2011, Graph, Image Segmentation, Speedup