CASIA OpenIR
Focal Boundary Guided Salient Object Detection
Wang, Yupei1,2; Zhao, Xin1,2; Hu, Xuecai3,4; Li, Yin5,6; Huang, Kaiqi2,7,8
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2019-06-01
Volume28Issue:6Pages:2813-2824
Corresponding AuthorHuang, Kaiqi(kqhuang@nlpr.ia.ac.cn)
AbstractThe 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.
KeywordVisual saliency detection salient object segmentation boundary detection deep learning
DOI10.1109/TIP.2019.2891055
WOS KeywordMODEL
Indexed BySCI
Language英语
Funding ProjectNational 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 OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Chinese Academy of Science
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000462386000014
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23350
Collection中国科学院自动化研究所
智能感知与计算研究中心
Corresponding AuthorHuang, Kaiqi
Affiliation1.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 AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationChinese 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.
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