Deep-MATEM: TEM query image based cross-modal retrieval for material science literature

doi:10.1007/s11042-018-6043-0

CASIA OpenIR > 模式识别实验室

	Deep-MATEM: TEM query image based cross-modal retrieval for material science literature
	Li, Hailiang 1,2; Guan, Qingxiao 2,3; Wang, Haidong 1; Dong, Jing2,4
发表期刊	MULTIMEDIA TOOLS AND APPLICATIONS
ISSN	1380-7501
	2018-12-01
卷号	77 期号:23 页码:30269-30290
通讯作者	Guan, Qingxiao(258817567@qq.com)
摘要	With the rapid increasing of published material science literatures, an effective literature retrieving system is important for researchers to obtain relevant information. In this paper we propose a cross-modal material science literatures retrieval method using transmission electron microscopy(TEM) image as query information, which provide a access of using material experiment generated TEM image data to retrieve literatures. In this method, terminologies are extracted and topic distribution are inferred from text part of literatures by using LDA, and we design a multi-task Convolutional Neuron Network(CNN) mapping query TEM image to the relevant terminologies and topic distribution predictions. The ranking score is calculated from output for query image and text data. Experimental results shows our method achieves better performance than multi-label CCA, Deep Semantic Matching(Deep SM) and Modality-Specific Deep Structure(MSDS).
关键词	Cross-Modal Document retrieval Convolutional network Material science
DOI	10.1007/s11042-018-6043-0
关键词[WOS]	SCALE ; CLASSIFICATION ; FEATURES
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U1536105] ; National Natural Science Foundation of China[51474237] ; National Natural Science Foundation of China[U1536120] ; National Natural Science Foundation of China[U1636201] ; National Key Research and Development Program of China[2016YFB1001003] ; National Natural Science Foundation of China[U1536105] ; National Natural Science Foundation of China[51474237] ; National Natural Science Foundation of China[U1536120] ; National Natural Science Foundation of China[U1636201] ; National Key Research and Development Program of China[2016YFB1001003]
项目资助者	National Natural Science Foundation of China ; National Key Research and Development Program of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000448401600006
出版者	SPRINGER
引用统计	被引频次：1[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/173211/22828
专题	模式识别实验室
通讯作者	Guan, Qingxiao
作者单位	1.Cent S Univ, Sch Minerals Proc & Bioengn, Changsha 410083, Hunan, Peoples R China 2.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing 100093, Peoples R China 3.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100093, Peoples R China 4.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Li, Hailiang,Guan, Qingxiao,Wang, Haidong,et al. Deep-MATEM: TEM query image based cross-modal retrieval for material science literature[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2018,77(23):30269-30290.
APA	Li, Hailiang,Guan, Qingxiao,Wang, Haidong,&Dong, Jing.(2018).Deep-MATEM: TEM query image based cross-modal retrieval for material science literature.MULTIMEDIA TOOLS AND APPLICATIONS,77(23),30269-30290.
MLA	Li, Hailiang,et al."Deep-MATEM: TEM query image based cross-modal retrieval for material science literature".MULTIMEDIA TOOLS AND APPLICATIONS 77.23(2018):30269-30290.