Journal
2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME)
Volume -, Issue -, Pages 199-204Publisher
IEEE
DOI: 10.1109/ICME.2019.00042
Keywords
PDNet; salient object detection; RGB-D; visual attention; depth
Categories
Funding
- Shenzhen Municipal Science and Technology Program [JCYJ20170818141146428]
- National Engineering Laboratory for Video Technology - Shenzhen Division
- Shenzhen Key Laboratory for Intelligent Multimedia and Virtual Reality [ZDSYS201703031405467]
- National Natural Science Foundation of China
- Guangdong Province Scientific Research on Big Data [U1611461]
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Fully convolutional neural networks (FCNs) have shown outstanding performance in many computer vision tasks including salient object detection. However, there still remains two issues needed to be addressed in deep learning based saliency detection. One is the lack of tremendous amount of annotated data to train a network. The other is the lack of robustness for extracting salient objects in images containing complex scenes. In this paper, we present a new architecture-PDNet, a robust prior-model guided depth-enhanced network for RGB-D salient object detection. In contrast to existing works, in which RGB-D values of image pixels are fed directly to a network, the proposed architecture is composed of a master network for processing RGB values, and a sub-network making full use of depth cues and incorporate depth-based features into the master network. To overcome the limited size of the labeled RGB-D dataset for training, we employ a large conventional RGB dataset to pre-train the master network, which proves to contribute largely to the final accuracy. Extensive evaluations over five benchmark datasets demonstrate that our proposed method performs favorably against the state-of-the-art approaches.
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