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

文献类型：期刊论文

作者Xue, Zhe1,4; Li, Guorong1,2,3; Huang, Qingming1,2,3
刊名INFORMATION SCIENCES
出版日期2018-07-01
卷号451页码:180-194
关键词Multi-view data Image annotation Representation learning Subspace learning Structure preserving
ISSN号0020-0255
DOI10.1016/j.ins.2018.03.051
英文摘要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.
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
语种英语
WOS记录号WOS:000432507900012
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.204/handle/2XEOYT63/5222]  
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.

入库方式： OAI收割

来源：计算技术研究所

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