High-level background prior based salient object detection |
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| Affiliation: | 1. Department of Computer Science, University of California, Irvine, USA;2. School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, China;3. Faculty of Information and Communication Technology, Mahidol University, Thailand;1. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China;2. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan;1. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong 510006, China;2. Department of Computer Science, Rutgers University, NJ 08854-8019, USA;3. School of Computer Engineering, Nanyang Technological University, 639798 Singapore, Singapore;4. School of Electronic Engineering, Xidian University, Xi’an, Shaanxi 710071, China;5. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, Guangdong 510642, China;1. Department of Computer Science and Information Engineering, National Dong Hwa University, Taiwan;2. Department of Computer Science, Jinan University, Guangzhou, China;3. Nanjing University of Information Science & Technology, Nanjing, China;4. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China |
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| Abstract: | Salient object detection is a fundamental problem in computer vision. Existing methods using only low-level features failed to uniformly highlight the salient object regions. In order to combine high-level saliency priors and low-level appearance cues, we propose a novel Background Prior based Salient detection method (BPS) for high-quality salient object detection.Different from other background prior based methods, a background estimation is added before performing saliency detection. We utilize the distribution of bounding boxes generated by a generic object proposal method to obtain background information. Three background priors are mainly considered to model the saliency, namely background connectivity prior, background contrast prior and spatial distribution prior, allowing the proposed method to highlight the salient object as a whole and suppress background clutters.Experiments conducted on two benchmark datasets validate that our method outperforms 11 state-of-the-art methods, while being more efficient than most leading methods. |
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| Keywords: | Salient object detection Background prior Superpixel Objectness |
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