RGB-D Saliency Detection Based on Integration Feature of Color and Depth Saliency Map
WU Jianguo SHAO Ting LIU Zhengyi
(Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei 230601, China)
(School of Computer Science and Technology, Anhui University, Hefei 230601, China)
Abstract：Depth information is proved to be an important part of human vision. However, most saliency detection methods based on 2D images do not make good use of depth information, thus an effective saliency detection method for RGB-D image is presented. It extracts color feature combined with depth saliency feature and detects salient objects based on photographic composition prior and background prior. First, original depth map is preprocessed to form depth saliency feature by background vertex area, photographic composition intersections, and compactness method. Then the association matrix is constructed by the adjacency weight of comprehensive feature. Manifold ranking is running from foreground view to form foreground saliency map based on photographic composition prior and fusion of depth saliency feature and color feature. In order to correct the error caused by assumption, the boundary connectivity is used to suppress background from background view. Final saliency map builds on fusion of foreground and background saliency map. Experiments compared with 4 different methods on RGB-D1000 database show that the proposed method has better precision-recall curve and outperforms the state- of-the-art methods.
吴建国,邵婷,刘政怡*. 融合显著深度特征的RGB-D图像显著目标检测[J]. 电子与信息学报, 2017, 39(9): 2148-2154.
WU Jianguo, SHAO Ting, LIU Zhengyi. RGB-D Saliency Detection Based on Integration Feature of Color and Depth Saliency Map. JEIT, 2017, 39(9): 2148-2154.
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