Deep-MATEM: TEM query image based cross-modal retrieval for material science literature | |
Li, Hailiang1,2; Guan, Qingxiao2,3; Wang, Haidong1; Dong, Jing2,4![]() | |
发表期刊 | MULTIMEDIA TOOLS AND APPLICATIONS
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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 | |
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. |
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