RGB-D Saliency Detection Based on Integration Feature of Color and Depth Saliency Map

WU Jianguo; SHAO Ting; LIU Zhengyi

doi:10.11999/JEIT161304

Volume 39 Issue 9

Sep. 2017

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Article Navigation > Journal of Electronics & Information Technology > 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[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2148-2154. doi: 10.11999/JEIT161304

Citation:

WU Jianguo, SHAO Ting, LIU Zhengyi. RGB-D Saliency Detection Based on Integration Feature of Color and Depth Saliency Map[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2148-2154. doi: 10.11999/JEIT161304

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RGB-D Saliency Detection Based on Integration Feature of Color and Depth Saliency Map

doi: 10.11999/JEIT161304 cstr: 32379.14.JEIT161304

Funds:

The National Key Technology RD Program (2015BAK24B00), The Specialized Research Fund for the Doctoral Program of Higher Education of China (20133401110009), Key Program of Natural Science Project of Educational Commission of Anhui Province (KJ2015A009), Open Funds of Co-Innovation Center for Information Supply Assurance Technology of Anhui University

Received Date: 2016-12-08
Rev Recd Date: 2017-05-22
Publish Date: 2017-09-19

Abstract

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.
- Salient object detection,
- Depth saliency map,
- Multi-layer center rectangle,
- Manifold ranking,
- Photographic composition prior,
- Background prior

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References(23)

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