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Multi-label double-layer learning for cross-modal retrieval

文献类型：期刊论文

作者He, Jianfeng1,2; Ma, Bingpeng1,2; Wang, Shuhui2; Liu, Yugui1; Huang, Qingming1,2
刊名NEUROCOMPUTING
出版日期2018-01-31
卷号275页码:1893-1902
关键词Cross-modal retrieval Multi-label Multimedia Partial least squares
ISSN号0925-2312
DOI10.1016/j.neucom.2017.10.032
英文摘要This paper proposes a novel method named Multi-label Double-layer Learning (MDLL) for multi-label cross-modal retrieval task. MDLL includes two stages (layers): L2C (Label to Common) and C2L (Common to Label). In the L2C stage, considering that labels can provide semantic information, we take label information as an auxiliary modality and apply a covariance matrix to represent label similarity in multi-label situation. Thus we can maximize the correlation of different modalities and reduce their semantic gap in the L2C stage. In addition, we find that samples with the same semantic labels may have different contents from users' view. According to this problem, in the C2L stage, labels are projected to a latent space learned from features of image and text. By this way, the label latent space are more related to the sample's contents. Then, it is noticed that the samples have same labels but various contents can be decreased. In MDLL, iterative learning of the L2C and C2L stages will improve the discriminative ability greatly and decline the discrepancy between the labels and the contents. To show the effectiveness of MDLL, some experiments are conducted on three multi-label cross-modal retrieval tasks (Pascal Voc 2007, Nus-wide, and LabelMe), on which competitive results are obtained. (C) 2017 Elsevier B.V. All rights reserved.
资助项目National Basic Research Program of China (973 Program)[2015CB351800] ; Natural Science Foundation of China (NSFC)[61332016] ; Natural Science Foundation of China (NSFC)[61572465] ; Natural Science Foundation of China (NSFC)[61672497] ; Natural Science Foundation of China (NSFC)[61620106009] ; Natural Science Foundation of China (NSFC)[U1636214] ; Natural Science Foundation of China (NSFC)[61650202] ; Key Research Program of Frontier Sciences[CAS: QYZDJ-SSW-SYS013]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000418370200176
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/5541]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ma, Bingpeng
作者单位1.Univ China Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 He, Jianfeng,Ma, Bingpeng,Wang, Shuhui,et al. Multi-label double-layer learning for cross-modal retrieval[J]. NEUROCOMPUTING,2018,275:1893-1902.
APA He, Jianfeng,Ma, Bingpeng,Wang, Shuhui,Liu, Yugui,&Huang, Qingming.(2018).Multi-label double-layer learning for cross-modal retrieval.NEUROCOMPUTING,275,1893-1902.
MLA He, Jianfeng,et al."Multi-label double-layer learning for cross-modal retrieval".NEUROCOMPUTING 275(2018):1893-1902.

入库方式： OAI收割

来源：计算技术研究所

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