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

doi:10.1007/s11280-015-0342-4

	A cross-media distance metric learning framework based on multi-view correlation mining and matching
	Zhang, Hong 1,2; Gao, Xingyu 3; Wu, Ping 1; Xu, Xin 1
	2016-03-01
发表期刊	WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
ISSN	1386-145X
卷号	19 期号:2 页码:181-197
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.
关键词	Cross-media Distance metric Sparse feature selection Multi-view matching
DOI	10.1007/s11280-015-0342-4
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering
WOS记录号	WOS:000370190000002
出版者	SPRINGER
引用统计	被引频次：20[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/2XEOYT63/8733
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.Hubei Prov Key Lab Intelligent Informat Proc & Re, Wuhan, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing, 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.