Saliency Object Detection Based on Bayesian Framework
Mao Zhen · Journal of Beijing University of Technology · 2014
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 theCenter-Surroundpixel model. The ROC and Precision-Rell test result on MSRA dataset show that this method outperforms all state-of-the-art approaches.