Structured Weak Semantic Space Construction for Visual Categorization
文献类型：期刊论文
| 作者 | Zhang, Chunjie1 ; Cheng, Jian2; Tian, Qi3
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2018-08-01 |
| 卷号 | 29期号:8页码:3442-3451 |
| 关键词 | Exemplar classifier training image classification structure learning visual categorization weak semantic space |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2017.2728060 |
| 通讯作者 | Zhang, Chunjie(chunjie.zhang@ia.ac.cn) |
| 英文摘要 | Visual features have been widely used for image representation and categorization. However, visual features are often inconsistent with human perception. Besides, constructing explicit semantic space is still an open problem. To alleviate these two problems, in this paper, we propose to construct structured weak semantic space for image representation. Exemplar classifier is first trained to separate each training image from other images for weak semantic space construction. However, each exemplar classifier separates one training image from other images, and it only has limited semantic separability. Besides, the outputs of exemplar classifiers are inconsistent with each other. We jointly construct the weak semantic space using structured constraint. This is achieved by imposing low-rank constraint on the outputs of exemplar classifiers with sparsity constraint. An alternative optimization procedure is used to learn the exemplar classifiers. Since the proposed method does not dependent on the initial image representation strategy, we can make use of various visual features for efficient exemplar classifier training (e.g., fisher vector-based methods and convolutional neural networks-based methods). We apply the proposed structured weak semantic space-based image representation method for categorization. The experimental results on several public image data sets prove the effectiveness of the proposed method. |
| WOS关键词 | IMAGE CLASSIFICATION ; OBJECT CATEGORIZATION ; LOW-RANK ; FEATURES ; RECOGNITION ; RETRIEVAL |
| 资助项目 | National Natural Science Foundation of China[61303154] ; National Natural Science Foundation of China[61332016] ; 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 and Blippar |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000439627700011 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Scientific Research Key Program of Beijing Municipal Commission of Education ; National Science Foundation of China ; ARO ; NEC Laboratories of America and Blippar |
| 源URL | [http://ir.ia.ac.cn/handle/173211/15317]  |
| 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, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.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. Structured Weak Semantic Space Construction for Visual Categorization[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(8):3442-3451. |
| APA | Zhang, Chunjie,Cheng, Jian,&Tian, Qi.(2018).Structured Weak Semantic Space Construction for Visual Categorization.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(8),3442-3451. |
| MLA | Zhang, Chunjie,et al."Structured Weak Semantic Space Construction for Visual Categorization".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.8(2018):3442-3451. |
入库方式： OAI收割
来源：自动化研究所
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