Transfer Learning for Visual Categorization: A Survey

doi:10.1109/TNNLS.2014.2330900

OPT OpenIR > 光谱成像技术研究室

	Transfer Learning for Visual Categorization: A Survey
	Shao, Ling 1,2; Zhu, Fan 2; Li, Xuelong3
	2015-05-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号	26 期号:5 页码:1019-1034
摘要	Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition.
文章类型	Article
关键词	Action Recognition Image Classification Machine Learning Object Recognition Survey Transfer Learning Visual Categorization
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TNNLS.2014.2330900
收录类别	SCI ; EI
关键词[WOS]	HUMAN ACTION RECOGNITION ; VIEW ACTION RECOGNITION ; DOMAIN ADAPTATION ; IMAGE CLASSIFICATION ; INVARIANT ANALYSIS ; FUZZY SYSTEM ; KERNEL ; REPRESENTATION ; MOTION ; HISTOGRAMS
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000353122400010
引用统计	被引频次：562[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/25044
专题	光谱成像技术研究室
作者单位	1.Nanjing Univ Informat Sci & Technol, Coll Elect & Informat Engn, Nanjing 210044, Jiangsu, Peoples R China 2.Univ Sheffield, Dept Elect & Elect Engn, Sheffield S1 3JD, S Yorkshire, England 3.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Shao, Ling,Zhu, Fan,Li, Xuelong. Transfer Learning for Visual Categorization: A Survey[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(5):1019-1034.
APA	Shao, Ling,Zhu, Fan,&Li, Xuelong.(2015).Transfer Learning for Visual Categorization: A Survey.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(5),1019-1034.
MLA	Shao, Ling,et al."Transfer Learning for Visual Categorization: A Survey".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.5(2015):1019-1034.

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