CASIA OpenIR  > 智能感知与计算研究中心
Learning Representative Deep Features for Image Set Analysis
Wu, Zifeng1; Huang, Yongzhen2; Wang, Liang2
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
2015-11-01
Volume17Issue:11Pages:1960-1968
SubtypeArticle
AbstractThis paper proposes to learn features from sets of labeled raw images. With this method, the problem of over-fitting can be effectively suppressed, so that deep CNNs can be trained from scratch with a small number of training data, i e., 420 labeled albums with about 30 000 photos. This method can effectively deal with sets of images, no matter if the sets bear temporal structures. A typical approach to sequential image analysis usually leverages motions between adjacent frames, while the proposed method focuses on capturing the co-occurrences and frequencies of features. Nevertheless, our method outperforms previous best performers in terms of album classification, and achieves comparable or even better performances in terms of gait based human identification. These results demonstrate its effectiveness and good adaptivity to different kinds of set data.
KeywordAlbum Classification Deep Learning Gait Recognition Image Set
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TMM.2015.2477681
WOS KeywordGAIT RECOGNITION ; CLASSIFICATION ; APPEARANCE ; CONTEXT
Indexed BySCI
Language英语
Funding OrganizationNational Basic Research Program of China(2012CB316300) ; National Natural Science Foundation of China(61135002 ; CCF-Tencent Open Fund ; 360 OpenLab Program ; 61420106015)
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000364102400009
Citation statistics
Cited Times:26[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10499
Collection智能感知与计算研究中心
Affiliation1.Univ Adelaide, Australian Ctr Visual Technol, Adelaide, SA 5005, Australia
2.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wu, Zifeng,Huang, Yongzhen,Wang, Liang. Learning Representative Deep Features for Image Set Analysis[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2015,17(11):1960-1968.
APA Wu, Zifeng,Huang, Yongzhen,&Wang, Liang.(2015).Learning Representative Deep Features for Image Set Analysis.IEEE TRANSACTIONS ON MULTIMEDIA,17(11),1960-1968.
MLA Wu, Zifeng,et al."Learning Representative Deep Features for Image Set Analysis".IEEE TRANSACTIONS ON MULTIMEDIA 17.11(2015):1960-1968.
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