Stereoscopic saliency model using contrast and depth-guided-background prior

Abstract

Abstract Many successful models of saliency have been proposed to detect salient regions for 2D images. Because stereopsis, with its distinctive depth information, influences human viewing, it is necessary for stereoscopic saliency detection to consider depth information as an additional cue. In this paper, we propose a 3D stereoscopic saliency model based on both contrast and depth-guided-background prior. First, a depth-guided-background prior is specifically detected from a disparity map apart from the conventional prior, assuming boundary super-pixels as background. Then, saliency based on disparity with the help of the proposed prior is proposed to prioritize the contrasts among super-pixels. In addition, a scheme to combine the contrast of disparity and the contrast of color is presented. Finally, 2D spatial dissimilarity features are further employed to refine the saliency map. Experimental results on the PSU stereo saliency benchmark dataset (SSB) show that the proposed method performs better than existing saliency models.