Region Diversity Maximization for Salient Object Detection

Salient object detection is an important technique for many content-based applications, but it becomes a challenging work when handling the cluttered saliency maps, which cannot completely highlight salient object regions and cannot suppress background regions. In this letter, we propose a novel approach to detect salient object from saliency map without manually setting any parameters. Region diversity maximization is used as the objective function to direct the object detection, and the optimal window for locating the salient object is obtained using an efficient iterative search scheme. Experimental results on different saliency maps demonstrate the overall better detection performance and computational efficiency of our approach.