RGB-D Saliency detection Based on Saliency Center Prior and Saliency-depth Probability Adjustment

LIU Zhengyi; HUANG Zichao; ZHANG Zhihua

doi:10.11999/JEIT170235

Volume 39 Issue 12

Dec. 2017

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Article Navigation > Journal of Electronics & Information Technology > 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[J]. Journal of Electronics & Information Technology, 2017, 39(12): 2945-2952. doi: 10.11999/JEIT170235

Citation:

LIU Zhengyi, HUANG Zichao, ZHANG Zhihua. RGB-D Saliency detection Based on Saliency Center Prior and Saliency-depth Probability Adjustment[J]. Journal of Electronics & Information Technology, 2017, 39(12): 2945-2952. doi: 10.11999/JEIT170235

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RGB-D Saliency detection Based on Saliency Center Prior and Saliency-depth Probability Adjustment

doi: 10.11999/JEIT170235

Funds:

The National Key Technology RD Program of the Ministry of Science and Technology of China (2015BAK24B00), The Key Program of Natural Science Project of Educational Commission of Anhui Province (KJ2015A009), The Open Issues on Co-Innovation Center for Information Supply Assurance Technology, Anhui University

Received Date: 2017-03-20
Rev Recd Date: 2017-07-04
Publish Date: 2017-12-19

Abstract

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.

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References(22)

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