Semantically Modeling of Object and Context for Categorization
文献类型：期刊论文
| 作者 | Zhang, Chunjie1,2 ; Cheng, Jian2,3,4; Tian, Qi5
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2019-04-01 |
| 卷号 | 30期号:4页码:1013-1024 |
| 关键词 | Context modeling object categorization object modeling semantic representation |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2018.2856096 |
| 通讯作者 | Zhang, Chunjie(chunjie.zhang@ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | IMAGE CLASSIFICATION ; LOW-RANK ; REPRESENTATION |
| 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 |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000461854100004 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Scientific Research Key Program of Beijing Municipal Commission of Education ; National Science Foundation of China ; ARO ; NEC Laboratories of America ; Blippar |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28059]  |
| 专题 | 类脑芯片与系统研究 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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