Saliency detection method fused depth information based on Bayesian framework

Abstract

In the complex background, the traditional saliency detection methods often encounter the problems of unstable detection results and low accuracy. To address this problem, a saliency detection method fused depth information based on Bayesian framework is proposed. Firstly, the color saliency map is obtained by using a variety of contrast methods which includes global contrast, local contrast and foreground-background contrast, and the depth saliency map is obtained by using the depth contrast method based on the anisotropic center-surround difference. Secondly, using the Bayesian model to fuse the color-based saliency map and the depth-based saliency map. The experimental results show that the proposed method can effectively detect the salient targets under complex background and achieve higher detection accuracy on the published NLPR-RGBD dataset and NJU-DS400 dataset.