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Cluster-sensitive Structured Correlation Analysis for Web cross-modal retrieval
Wang, Shuhui1; Zhuang, Fuzhen1; Jiang, Shuqiang1; Huang, Qingming1,2; Tian, Qi3
2015-11-30
发表期刊NEUROCOMPUTING
ISSN0925-2312
卷号168页码:747-760
摘要Modern cross-modal retrieving technology is required to find semantically relevant content from heterogeneous modalities. As previous studies construct unified dense correlation models on small scale cross-modal data, they are not capable of processing large scale Web data, because (a) the content of Web cross media is divergent; (b) the topic sensitive structure information in the high dimensional space is neglected; and (c) data should be organized as strictly corresponding pairs, which is not satisfied in real world scenarios. To address these challenges, we propose a cluster-sensitive cross-modal correlation learning framework. First, a set of cluster-sensitive correlation sub-models are learned instead of a unified correlation model, which better fits the content divergence in different modalities. We impose structured sparsity regularization on the projection vectors to learn a set of interpretable structured sparse correlation sub-models. Second, to compensate for the correspondence missing, we take full advantage of both intra-modal affinity and inter-modal co-occurrence. The projected coordinates of adjacent data within a modality tend to be similar, and the inconsistency of cluster-sensitive projection is minimized. The learned correlation model adapts to the content divergence and thus achieves better model generality and bias-variance trade-off. Extensive experiments on two large scale cross-modal data demonstrate the effectiveness of our approach. (C) 2015 Elsevier B.V. All rights reserved.
关键词Correlation learning Cluster-sensitive Structured correlation model Correspondence missing
DOI10.1016/j.neucom.2015.05.049
收录类别SCI
语种英语
资助项目National Basic Research Program of China (973 Program)[2012CB316400] ; National Basic Research Program of China (973 Program)[2015CB351802] ; 863 program of China[2014AA015202] ; National Natural Science Foundation of China (NSFC)[61025011] ; National Natural Science Foundation of China (NSFC)[61303160] ; National Natural Science Foundation of China (NSFC)[61332016] ; National Natural Science Foundation of China (NSFC)[61390511] ; National Natural Science Foundation of China (NSFC)[61322212] ; National Natural Science Foundation of China (NSFC)[61473273] ; National Natural Science Foundation of China (NSFC)[61429201] ; ARO Grant[W911NF-12-1-0057] ; NEC Laboratories of America
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000359165000074
出版者ELSEVIER SCIENCE BV
引用统计
被引频次:9[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/9520
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Shuhui
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intellectual Informat Proc, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
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Wang, Shuhui,Zhuang, Fuzhen,Jiang, Shuqiang,et al. Cluster-sensitive Structured Correlation Analysis for Web cross-modal retrieval[J]. NEUROCOMPUTING,2015,168:747-760.
APA Wang, Shuhui,Zhuang, Fuzhen,Jiang, Shuqiang,Huang, Qingming,&Tian, Qi.(2015).Cluster-sensitive Structured Correlation Analysis for Web cross-modal retrieval.NEUROCOMPUTING,168,747-760.
MLA Wang, Shuhui,et al."Cluster-sensitive Structured Correlation Analysis for Web cross-modal retrieval".NEUROCOMPUTING 168(2015):747-760.
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