Semantically Modeling of Object and Context for Categorization

doi:10.1109/TNNLS.2018.2856096

	Semantically Modeling of Object and Context for Categorization
	Zhang, Chunjie1,2 ; Cheng, Jian2,3,4 ; Tian, Qi 5
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2019-04-01
卷号	30 期号:4 页码:1013-1024
通讯作者	Zhang, Chunjie(chunjie.zhang@ia.ac.cn)
摘要	Object-centric-based categorization methods have been proven more effective than hard partitions of images (e.g., spatial pyramid matching). However, how to determine the locations of objects is still an open problem. Besides, modeling of context areas is often mixed with the background. Moreover, the semantic information is often ignored by these methods that only use visual representations for classification. In this paper, we propose an object categorization method by semantically modeling the object and context information (SOC). We first select a number of candidate regions with high confidence scores and semantically represent these regions by measuring correlations of each region with prelearned classifiers (e.g., local feature-based classifiers and deep convolutional-neural-network-based classifiers). These regions are clustered for object selections. The other selected areas are then viewed as context areas. We treat other areas beyond the object and context areas within one image as the background. The visually and semantically represented objects and contexts are then used along with the background area for object representations and categorizations. Experimental results on several public data sets well demonstrate the effectiveness of the proposed object categorization method by semantically modeling the object and context information.
关键词	Context modeling object categorization object modeling semantic representation
DOI	10.1109/TNNLS.2018.2856096
关键词[WOS]	IMAGE CLASSIFICATION ; LOW-RANK ; REPRESENTATION
收录类别	SCI
语种	英语
资助项目	Scientific Research Key Program of Beijing Municipal Commission of Education[KZ201610005012] ; National Science Foundation of China[61429201] ; ARO[W911NF-15-1-0290] ; NEC Laboratories of America ; Blippar ; Scientific Research Key Program of Beijing Municipal Commission of Education[KZ201610005012] ; National Science Foundation of China[61429201] ; ARO[W911NF-15-1-0290] ; NEC Laboratories of America ; Blippar
项目资助者	Scientific Research Key Program of Beijing Municipal Commission of Education ; National Science Foundation of China ; ARO ; NEC Laboratories of America ; Blippar
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000461854100004
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	机器学习
引用统计	被引频次：9[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/173211/28060
专题	复杂系统认知与决策实验室_高效智能计算与学习
通讯作者	Zhang, Chunjie
作者单位	1.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China 5.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
推荐引用方式 GB/T 7714	Zhang, Chunjie,Cheng, Jian,Tian, Qi. Semantically Modeling of Object and Context for Categorization[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(4):1013-1024.
APA	Zhang, Chunjie,Cheng, Jian,&Tian, Qi.(2019).Semantically Modeling of Object and Context for Categorization.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(4),1013-1024.
MLA	Zhang, Chunjie,et al."Semantically Modeling of Object and Context for Categorization".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.4(2019):1013-1024.