CASIA OpenIR  > 智能系统与工程
Focal Boundary Guided Salient Object Detection
Wang, Yupei1,2; Zhao, Xin1,2; Hu, Xuecai3,4; Li, Yin5,6; Huang, Kaiqi2,7,8
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2019-06-01
卷号28期号:6页码:2813-2824
通讯作者Huang, Kaiqi(kqhuang@nlpr.ia.ac.cn)
摘要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.
关键词Visual saliency detection salient object segmentation boundary detection deep learning
DOI10.1109/TIP.2019.2891055
关键词[WOS]MODEL
收录类别SCI
语种英语
资助项目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]
项目资助者National Key Research and Development Program of China ; National Natural Science Foundation of China ; Chinese Academy of Science
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000462386000014
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类图像视频处理与分析
引用统计
被引频次:33[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/23350
专题智能系统与工程
通讯作者Huang, Kaiqi
作者单位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
第一作者单位中国科学院自动化研究所
通讯作者单位模式识别国家重点实验室
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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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