Saliency Object Detection Based on Bayesian Framework

Saliency detection has gained a great deal of attention in computer vision in images and videos. It is a valuable tool in image processing, such as object segmentation, suspicious detection, image retrieval, etc. This paper proposes a saliency object detection algorithm that combines the BottomUp passive perception with the Top-Down active perception, together into Bayesian framework, detecting the salient object coarse-to-fine. The method is efficiently implemented by using the kernel density estimation and the“Center-Surround”pixel model. The ROC and Precision-Rell test result on MSRA dataset show that this method outperforms all state-of-the-art approaches.