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Multiview, Few-Labeled Object Categorization by Predicting Labels With View Consistency
Zhang, Chunjie1,2; Cheng, Jian3,4,5; Tian, Qi6
发表期刊IEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
2019-11-01
卷号49期号:11页码:3834-3843
通讯作者Zhang, Chunjie(chunjie.zhang@ia.ac.cn)
摘要The categorization accuracies of objects have been greatly improved in recent years. However, large quantities of labeled images are needed. many methods fail when only few labeled images are available. To tackle the few-labeled object categorization problem, we need to represent and classify them from multiple views. In this paper, we propose a novel multiview, few-labeled object categorization algorithm by predicting the labels of images with view consistency (MVFL-VC). We use labeled images along with other unlabeled images in a unified framework. A mapping function is learned to model the correlations of images with their labels. Since there are no labeling information for unlabeled images, we simultaneously learn the mapping function and image labels by classification error minimization. We make use of multiview information for joint object categorization. Although different views represent different aspects of images, for one image, the predicted categories of multiple views should be consistent with each other. We learn the mapping function by minimizing the summed classification losses along with the discrepancy of predicted labels between different views in an alternative way. We conduct object categorization experiments on five public image datasets and compare with other semi-supervised methods. Experimental results well demonstrate the effectiveness of the proposed MVFL-VC method.
关键词Few-labeled images multiview classification object categorization view consistency
DOI10.1109/TCYB.2018.2845912
关键词[WOS]CONSTRAINED LOW-RANK ; IMAGE CLASSIFICATION ; REPRESENTATION ; PROPAGATION ; FRAMEWORK ; FEATURES
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Automation & Control Systems ; Computer Science
WOS类目Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号WOS:000476811000001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类图像视频处理与分析
引用统计
被引频次:14[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/173211/27807
专题高效智能计算与学习
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.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
6.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
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GB/T 7714
Zhang, Chunjie,Cheng, Jian,Tian, Qi. Multiview, Few-Labeled Object Categorization by Predicting Labels With View Consistency[J]. IEEE TRANSACTIONS ON CYBERNETICS,2019,49(11):3834-3843.
APA Zhang, Chunjie,Cheng, Jian,&Tian, Qi.(2019).Multiview, Few-Labeled Object Categorization by Predicting Labels With View Consistency.IEEE TRANSACTIONS ON CYBERNETICS,49(11),3834-3843.
MLA Zhang, Chunjie,et al."Multiview, Few-Labeled Object Categorization by Predicting Labels With View Consistency".IEEE TRANSACTIONS ON CYBERNETICS 49.11(2019):3834-3843.
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