Learning Representative Deep Features for Image Set Analysis

doi:10.1109/TMM.2015.2477681

CASIA OpenIR > 模式识别实验室

	Learning Representative Deep Features for Image Set Analysis
	Wu, Zifeng 1; Huang, Yongzhen2 ; Wang, Liang2
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
	2015-11-01
卷号	17 期号:11 页码:1960-1968
文章类型	Article
摘要	This 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.
关键词	Album Classification Deep Learning Gait Recognition Image Set
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TMM.2015.2477681
关键词[WOS]	GAIT RECOGNITION ; CLASSIFICATION ; APPEARANCE ; CONTEXT
收录类别	SCI
语种	英语
项目资助者	National Basic Research Program of China(2012CB316300) ; National Natural Science Foundation of China(61135002 ; CCF-Tencent Open Fund ; 360 OpenLab Program ; 61420106015)
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000364102400009
引用统计	被引频次：72[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/173211/10499
专题	模式识别实验室
作者单位	1.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
推荐引用方式 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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