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Image Class Prediction by Joint Object, Context, and Background Modeling
Zhang, Chunjie1,2,3; Zhu, Guibo4; Liang, Chao5; Zhang, Yifan6; Huang, Qingming2; Tian, Qi7
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
2018-02-01
Volume28Issue:2Pages:428-438
SubtypeArticle
AbstractState-of-the-art image classification methods often use spatial pyramid matching or its variants to make use of the spatial layout of visual features. However, objects may appear at various places with different scales and orientations. Besides, traditionally object-centric-based methods only consider objects and the background without fully exploring the context information. To solve these problems, in this paper we propose a novel image classification method by jointly modeling the object, context, and background information (OCB). OCB consists of three components: 1) locate the positions of objects; 2) determine the context areas of objects; and 3) treat the other areas as the background. We use objectness proposal techniques to select candidate bounding boxes. Boxes with high confidence scores are combined to determine objects' positions. To select the context areas, we use candidate boxes that have relatively lower confidence scores compared with boxes for object location selection. The other areas are viewed as the background. We jointly combine the object, context, and background for image representation and classification. Experiments on six data sets well demonstrate the superiority of the proposed OCB method over other spatial partition methods.
KeywordBackground Modeling Context Modeling Image Class Prediction Object Modeling
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TCSVT.2016.2613125
WOS KeywordCLASSIFICATION ; FEATURES
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61303154)
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000425036400013
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15314
Collection类脑智能研究中心
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, Beijing 100190, Peoples R China
5.Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp, Wuhan 430072, Hubei, Peoples R China
6.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
7.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Chunjie,Zhu, Guibo,Liang, Chao,et al. Image Class Prediction by Joint Object, Context, and Background Modeling[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2018,28(2):428-438.
APA Zhang, Chunjie,Zhu, Guibo,Liang, Chao,Zhang, Yifan,Huang, Qingming,&Tian, Qi.(2018).Image Class Prediction by Joint Object, Context, and Background Modeling.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,28(2),428-438.
MLA Zhang, Chunjie,et al."Image Class Prediction by Joint Object, Context, and Background Modeling".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 28.2(2018):428-438.
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