Image Class Prediction by Joint Object, Context, and Background Modeling

doi:10.1109/TCSVT.2016.2613125

	Image Class Prediction by Joint Object, Context, and Background Modeling
	Zhang, Chunjie1,2,3 ; Zhu, Guibo4 ; Liang, Chao 5; Zhang, Yifan6 ; Huang, Qingming 2; Tian, Qi 7
刊名	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
	2018-02-01
卷号	28 期号:2 页码:428-438
关键词	Background Modeling Context Modeling Image Class Prediction Object Modeling
DOI	10.1109/TCSVT.2016.2613125
文献子类	Article
英文摘要	State-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.
WOS关键词	CLASSIFICATION ; FEATURES
WOS研究方向	Engineering
语种	英语
WOS记录号	WOS:000425036400013
资助机构	National Natural Science Foundation of China(61303154)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/15314]
专题	自动化研究所_类脑智能研究中心
作者单位	1.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
推荐引用方式 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.