Knowledge Commons of Institute of Automation，CAS
| Focal Boundary Guided Salient Object Detection | |
Wang, Yupei1,2 ; Zhao, Xin1,2; Hu, Xuecai3,4; Li, Yin5,6; Huang, Kaiqi2,7,8
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| Source Publication | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| ISSN | 1057-7149 |
| 2019-06-01 | |
| Volume | 28Issue:6Pages:2813-2824 |
| Corresponding Author | Huang, Kaiqi(kqhuang@nlpr.ia.ac.cn) |
| Abstract | The performance of salient object segmentation has been significantly advanced by using the deep convolutional networks. However, these networks often produce blob-like saliency maps without accurate object boundaries. This is caused by the limited spatial resolution of their feature maps after multiple pooling operations and might hinder downstream applications that require precise object shapes. To address this issue, we propose a novel deep model-Focal Boundary Guided (Focal-BG) network. Our model is designed to jointly learn to segment salient object masks and detect salient object boundaries. Our key idea is that additional knowledge about object boundaries can help to precisely identify the shape of the object. Moreover, our model incorporates a refinement pathway to refine the mask prediction and makes use of the focal lass to facilitate the learning of the hard boundary pixels. To evaluate our model, we conduct extensive experiments. Our Focal-BG network consistently outperforms the state-of-the-art methods on five major benchmarks. We provide a detailed analysis of these results and demonstrate that our joint modeling of salient object boundary and mask helps to better capture the shape details, especially in the vicinity of object boundaries. |
| Keyword | Visual saliency detection salient object segmentation boundary detection deep learning |
| DOI | 10.1109/TIP.2019.2891055 |
| WOS Keyword | MODEL |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key Research and Development Program of China[2016YFB1001005] ; National Natural Science Foundation of China[61673375] ; National Natural Science Foundation of China[61602485] ; National Natural Science Foundation of China[61721004] ; Chinese Academy of Science[QYZDB-SSW-JSC006] ; Chinese Academy of Science[173211KYSB20160008] ; National Key Research and Development Program of China[2016YFB1001005] ; National Natural Science Foundation of China[61673375] ; National Natural Science Foundation of China[61602485] ; National Natural Science Foundation of China[61721004] ; Chinese Academy of Science[QYZDB-SSW-JSC006] ; Chinese Academy of Science[173211KYSB20160008] |
| Funding Organization | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Chinese Academy of Science |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000462386000014 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/23350 |
| Collection | 中国科学院自动化研究所 智能感知与计算 |
| Corresponding Author | Huang, Kaiqi |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Syst & Engn, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Anhui, Peoples R China 5.Univ Wisconsin, Dept Biostat & Med Informat, Madison, WI 53706 USA 6.Univ Wisconsin, Dept Comp Sci, 1210 W Dayton St, Madison, WI 53706 USA 7.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Syst & Engn, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 8.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 20031, Peoples R China |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Wang, Yupei,Zhao, Xin,Hu, Xuecai,et al. Focal Boundary Guided Salient Object Detection[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(6):2813-2824. |
| APA | Wang, Yupei,Zhao, Xin,Hu, Xuecai,Li, Yin,&Huang, Kaiqi.(2019).Focal Boundary Guided Salient Object Detection.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(6),2813-2824. |
| MLA | Wang, Yupei,et al."Focal Boundary Guided Salient Object Detection".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.6(2019):2813-2824. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| 08603790.pdf（3275KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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