Saliency Cuts: An automatic approach to object segmentation

Yu Fu, Jian Cheng, Zhenglong Li, Hanqing Lu

Abstract: Interactive graph cuts are widely used in object segmentation but with some disadvantages: 1) Manual interactions may cause inaccurate or even incorrect segmentation results and involve more interactions especially for novices. 2) In some situations, the manual interactions are infeasible. To overcome these disadvantages, we propose a novel approach, namely Saliency cuts, to segment object from background automatically. By exploring the effects of labels to graph cuts, the so called ldquoprofessional labelsrdquo is introduced to evaluate labels. With the aid of saliency detection, a multiresolution framework is designed to provide ldquoprofessional labelsrdquo automatically and implement object segmentation using graph cuts. The experiments demonstrate the promising performance of Saliency cuts.