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Deep-MATEM: TEM query image based cross-modal retrieval for material science literature
Li, Hailiang1,2; Guan, Qingxiao2,3; Wang, Haidong1; Dong, Jing2,4
Source PublicationMULTIMEDIA TOOLS AND APPLICATIONS
ISSN1380-7501
2018-12-01
Volume77Issue:23Pages:30269-30290
Corresponding AuthorGuan, Qingxiao(258817567@qq.com)
AbstractWith 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).
KeywordCross-Modal Document retrieval Convolutional network Material science
DOI10.1007/s11042-018-6043-0
WOS KeywordSCALE ; CLASSIFICATION ; FEATURES
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Program of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000448401600006
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22828
Collection智能感知与计算研究中心
Corresponding AuthorGuan, Qingxiao
Affiliation1.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
Recommended Citation
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.
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