CASIA OpenIR  > 高效智能计算与学习
Multiview Semantic Representation for Visual Recognition
Zhang, Chunjie1,2; Cheng, Jian2,3,4; Tian, Qi5
发表期刊IEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
2020-05-01
卷号50期号:5页码:2038-2049
通讯作者Zhang, Chunjie(chunjie.zhang@ia.ac.cn)
摘要Due to interclass and intraclass variations, the images of different classes are often cluttered which makes it hard for efficient classifications. The use of discriminative classification algorithms helps to alleviate this problem. However, it is still an open problem to accurately model the relationships between visual representations and human perception. To alleviate these problems, in this paper, we propose a novel multiview semantic representation (MVSR) algorithm for efficient visual recognition. First, we leverage visually based methods to get initial image representations. We then use both visual and semantic similarities to divide images into groups which are then used for semantic representations. We treat different image representation strategies, partition methods, and numbers as different views. A graph is then used to combine the discriminative power of different views. The similarities between images can be obtained by measuring the similarities of graphs. Finally, we train classifiers to predict the categories of images. We evaluate the discriminative power of the proposed MVSR method for visual recognition on several public image datasets. Experimental results show the effectiveness of the proposed method.
关键词Image classification multiview object categorization semantic representation visual recognition
DOI10.1109/TCYB.2018.2875728
关键词[WOS]IMAGE CLASSIFICATION ; LOW-RANK ; OBJECT CATEGORIZATION ; FUSION ; FACE
收录类别SCI
语种英语
资助项目National Science Foundation of China[61872362] ; National Science Foundation of China[61429201] ; ARO[W911NF-15-1-0290] ; NEC Laboratory of America ; NEC Laboratory of Blippar
项目资助者National Science Foundation of China ; ARO ; NEC Laboratory of America ; NEC Laboratory of 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:000528622000023
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:3[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/173211/39346
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, 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. Multiview Semantic Representation for Visual Recognition[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,50(5):2038-2049.
APA Zhang, Chunjie,Cheng, Jian,&Tian, Qi.(2020).Multiview Semantic Representation for Visual Recognition.IEEE TRANSACTIONS ON CYBERNETICS,50(5),2038-2049.
MLA Zhang, Chunjie,et al."Multiview Semantic Representation for Visual Recognition".IEEE TRANSACTIONS ON CYBERNETICS 50.5(2020):2038-2049.
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