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A cross-media distance metric learning framework based on multi-view correlation mining and matching

文献类型：期刊论文

作者Zhang, Hong1,3; Gao, Xingyu2; Wu, Ping1; Xu, Xin1
刊名WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
出版日期2016-03-01
卷号19期号:2页码:181-197
关键词Cross-media Distance metric Sparse feature selection Multi-view matching
ISSN号1386-145X
DOI10.1007/s11280-015-0342-4
英文摘要With the explosion of multimedia data, it is usual that different multimedia data often coexist in web repositories. Accordingly, it is more and more important to explore underlying intricate cross-media correlation instead of single-modality distance measure so as to improve multimedia semantics understanding. Cross-media distance metric learning focuses on correlation measure between multimedia data of different modalities. However, the existence of content heterogeneity and semantic gap makes it very challenging to measure cross-media distance. In this paper, we propose a novel cross-media distance metric learning framework based on sparse feature selection and multi-view matching. First, we employ sparse feature selection to select a subset of relevant features and remove redundant features for high-dimensional image features and audio features. Secondly, we maximize the canonical coefficient during image-audio feature dimension reduction for cross-media correlation mining. Thirdly, we further construct a Multi-modal Semantic Graph to find embedded manifold cross-media correlation. Moreover, we fuse the canonical correlation and the manifold information into multi-view matching which harmonizes different correlations with an iteration process and build Cross-media Semantic Space for cross-media distance measure. The experiments are conducted on image-audio dataset for cross-media retrieval. Experiment results are encouraging and show that the performance of our approach is effective.
资助项目National Natural Science Foundation of China[61373109] ; National Natural Science Foundation of China[61003127] ; National Natural Science Foundation of China[61273303] ; National Natural Science Foundation of China[61440016] ; State Key Laboratory of Software Engineering[SKLSE2012-09-31] ; Program for Outstanding Young Science and Technology Innovation Teams in Higher Education Institutions of Hubei Province, China[T201202] ; Natural Science Foundation of Hubei Provincial of China[2014CFB247]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000370190000002
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/8732]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Hong
作者单位1.Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan 430081, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Hubei Prov Key Lab Intelligent Informat Proc & Re, Wuhan, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Hong,Gao, Xingyu,Wu, Ping,et al. A cross-media distance metric learning framework based on multi-view correlation mining and matching[J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,2016,19(2):181-197.
APA Zhang, Hong,Gao, Xingyu,Wu, Ping,&Xu, Xin.(2016).A cross-media distance metric learning framework based on multi-view correlation mining and matching.WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,19(2),181-197.
MLA Zhang, Hong,et al."A cross-media distance metric learning framework based on multi-view correlation mining and matching".WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 19.2(2016):181-197.

入库方式： OAI收割

来源：计算技术研究所

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