Saliency Density Maximization for Efficient Visual Objects Discovery

Abstract

Detection of salient objects in an image remains a challenging problem despite extensive studies in visual saliency, as the generated saliency map is usually noisy and incomplete. In this paper, we propose a new method to discover the salient object without prior knowledge on its shape and size. By searching the sub-image, i.e., a bounding box of maximum saliency density, the new formulation can automatically crop the salient objects of various sizes in spite of the cluttered background, and is capable to handle different types of saliency maps. A global optimal solution is obtained by the proposed density-based branch-and-bound search. The proposed method can apply to both images and videos. Experimental results on a public dataset of 5000 images show that our unsupervised detection approach is comparable to the state-of-the-art learning-based methods. Promising results are also observed in the salient object detection for videos with a good potential in video retargeting.