RGB-D Saliency detection Based on Saliency Center Prior and Saliency-depth Probability Adjustment
LIU Zhengyi HUANG Zichao ZHANG Zhihua
(Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230601, China)
(Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei 230601, China)
Abstract：Along with more and more important role of depth features played in computer saliency community, traditional RGB saliency models can not directly utilized for saliency detection on RGB-D domains. This paper proposes saliency center prior and Saliency-Depth (S-D) probability adjustment RGB-D saliency detection framework, making the depth and RGB features adaptively fuse and complementary to each other. First, the initial saliency maps of depth images are obtained according to three-dimension space weights and depth prior; second, the feature fused Manifold Ranking model with extracted depth features is utilized for RGB image saliency detection. Then, the saliency center prior based on depth is computed and this value is used as saliency weight to further improve the RGB image saliency detection results, obtaining the final RGB saliency map. After that, Saliency-Depth (S-D) rectify probability is also computed and the saliency results of depth images are corrected with this probability. Then the saliency center prior based on RGB is also computed and this value is used as saliency weights to further improve the depth image saliency detection results and to obtain the final depth saliency maps. Finally the optimization framework is utilized to optimize the depth image final saliency maps and to obtain the final RGB-D saliency map. All the experiments are executed on the public NLPR RGBD-1000 benchmark and extensive experiments demonstrate that the proposed algorithm achieves better performance compared with existing state-of-the-art approaches.
刘政怡,黄子超,张志华. 显著中心先验和显著-深度概率矫正的RGB-D显著目标检测[J]. 电子与信息学报, 2017, 39(12): 2945-2952.
LIU Zhengyi, HUANG Zichao, ZHANG Zhihua. RGB-D Saliency detection Based on Saliency Center Prior and Saliency-depth Probability Adjustment. JEIT, 2017, 39(12): 2945-2952.
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