Saliency Density Maximization for Object Detection and Localization

Accurate localization of the salient object from an image is a difficult problem when the saliency map is noisy and incomplete. A fast approach to detect salient objects from images is proposed in this paper. To well balance the size of the object and the saliency it contains, the salient object detection is first formulated with the maximum saliency density on the saliency map. To obtain the global optimal solution, a branch-and-bound search algorithm is developed to speed up the detection process. Without any prior knowledge provided, the proposed method can effectively and efficiently detect salient objects from images. Extensive results on different types of saliency maps with a public dataset of five thousand images show the advantages of our approach as compared to some state-of-the-art methods.

• Type: Journal
• Year: 2010
• Where: ACCV
• Authors: Ye Luo, Junsong Yuan, Ping Xue, Qi Tian
• Keywords: ACCV 2010, Computer Vision, Images, Saliency Maps, Salient Objects