Joint multi-view representation and image annotation via optimal predictive subspace learning

doi:10.1016/j.ins.2018.03.051

	Joint multi-view representation and image annotation via optimal predictive subspace learning
	Xue, Zhe 1,4; Li, Guorong 1,2,3; Huang, Qingming 1,2,3
	2018-07-01
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
卷号	451 页码:180-194
摘要	Image representation and annotation are two key tasks in practical applications such as image search. Existing methods have tried to learn an effective representation or to predict tags directly using multi-view low-level visual features, which usually contain redundant information. However, these two tasks are closely related and interact on each other. A suitable image representation can yield better image annotation results, which in turn can effectively guide the image representation learning. In this paper, we propose to jointly conduct multi-view representation and image annotation via optimal predictive subspace learning, making the two tasks promote each other. Specifically, for subspace learning, visual structure and semantic information of images are exploited to make the learned subspace more discriminative and compact. For tag prediction, support vector machines (SVM) is adopted to obtain better tag prediction results. Then to simultaneously learn image representation, tag predictors and projection function, the three subproblems are combined into a unified optimization objective function and an alternative optimization algorithm is derived to solve it. Experimental results on four image datasets illustrate that our method is superior to the other image annotation methods. (C) 2018 Elsevier Inc. All rights reserved.
关键词	Multi-view data Image annotation Representation learning Subspace learning Structure preserving
DOI	10.1016/j.ins.2018.03.051
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61772494] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61650202] ; National Natural Science Foundation of China[61532006] ; National Natural Science Foundation of China[61772083] ; National Basic Research Program of China (973 Program)[2015CB351800] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-SYS013] ; Youth Innovation Promotion Association CAS ; Director Foundation of Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia[1TSM20180102]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000432507900012
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/5223
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Guorong; Huang, Qingming
作者单位	1.Univ Chinese Acad Sci CAS, Sch Comp & Control Engn, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Comput Tech, Key Lab Intell Info Proc, Beijing 100080, Peoples R China 3.Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R China 4.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China
推荐引用方式 GB/T 7714	Xue, Zhe,Li, Guorong,Huang, Qingming. Joint multi-view representation and image annotation via optimal predictive subspace learning[J]. INFORMATION SCIENCES,2018,451:180-194.
APA	Xue, Zhe,Li, Guorong,&Huang, Qingming.(2018).Joint multi-view representation and image annotation via optimal predictive subspace learning.INFORMATION SCIENCES,451,180-194.
MLA	Xue, Zhe,et al."Joint multi-view representation and image annotation via optimal predictive subspace learning".INFORMATION SCIENCES 451(2018):180-194.