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Structured Weak Semantic Space Construction for Visual Categorization
Zhang, Chunjie1; Cheng, Jian2; Tian, Qi3
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
2018-08-01
Volume29Issue:8Pages:3442-3451
Corresponding AuthorZhang, Chunjie(chunjie.zhang@ia.ac.cn)
AbstractVisual 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.
KeywordExemplar classifier training image classification structure learning visual categorization weak semantic space
DOI10.1109/TNNLS.2017.2728060
WOS KeywordIMAGE CLASSIFICATION ; OBJECT CATEGORIZATION ; LOW-RANK ; FEATURES ; RECOGNITION ; RETRIEVAL
Indexed BySCI
Language英语
Funding ProjectNational 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
Funding OrganizationNational 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
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000439627700011
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:13[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15317
Collection类脑智能研究中心
Corresponding AuthorZhang, Chunjie
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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