Automatic segmentation of salient objects using iterative reversible graph cut
Chanho Jung, Beomjoon Kim, Changick Kim · 2010
There have been several interactive approaches to extracting objects from still images, since it is significantly difficult to automatically segment objects in complex background. In this paper, we present a novel automatic scheme for extracting salient objects from natural images. To this end, segmentation of salient objects is formulated as a global energy minimization problem in an iterative self-adaptive framework. By employing a saliency detection technique, object and background seeds are inferred automatically. The problem in this step is that the automatically generated seeds may not be reliably positioned. An iterative reversible graph cut method is introduced to overcome the problem inherent in the saliency-based seed extraction method. In the iterative self-adaptive framework, bidirectional state transitions are iteratively involved to reduce the mis-classified pixels. Experimental results show that the proposed segmentation method yields more accurate segmentation results than previous segmentation approaches.