RGB-D saliency detection via mutual guided manifold ranking
Haoyang Xue, Yun Gu, Yijun Li, Jie Yang · 2015
Visual saliency detection has gained its popularity in computer vision in recent years. Depth information is proven as a fundamental element of human vision while it is underutilized in existing saliency detection approaches. In this paper, an effective visual object saliency detection model via RGB and depth cues mutual guided manifold ranking is proposed. The depth features are extracted to guide the saliency ranking of RGB image while the RGB saliency is used as the guide of depth map ranking as well. We obtain the final result by fusing the RGB and depth saliency maps. The experimental result on a benchmark dataset which contains 1000 RGB-D images demonstrates the effectiveness and superior performance compared with several state-of-art methods.