Saliency cuts based on adaptive triple thresholding

Salient object detection attracts much attention for its effectiveness in numerous applications. However, how to effectively produce a high quality binary mask from a saliency map, named saliency cuts, is still an open problem. In this paper, we propose a novel saliency cuts approach using unsupervised seeds generation and GrabCut algorithm. With the input of a saliency map, we produce seeds for segmentation using adaptive triple thresholding, and feed the seeds to GrabCut algorithm. Finally, a high quality object mask is generated by iteratively optimization. The experimental results show that the proposed approach is competent to the task of saliency cuts and outperforms the state-of-the-art methods.