Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval
	Kang, Cuicui; Xiang, Shiming; Liao, Shengcai; Xu, Changsheng; Pan, Chunhong
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
	2015-03-01
卷号	17 期号:3 页码:370-381
文章类型	Article
摘要	The cross-modal feature matching has gained much attention in recent years, which has many practical applications, such as the text-to-image retrieval. The most difficult problem of cross-modal matching is how to eliminate the heterogeneity between modalities. The existing methods (e.g., CCA and PLS) try to learn a common latent subspace, where the heterogeneity between two modalities is minimized so that cross-matching is possible. However, most of these methods require fully paired samples and suffer difficulties when dealing with unpaired data. Besides, utilizing the class label information has been found as a good way to reduce the semantic gap between the low-level image features and high-level document descriptions. Considering this, we propose a novel and effective supervised algorithm, which can also deal with the unpaired data. In the proposed formulation, the basis matrices of different modalities are jointly learned based on the training samples. Moreover, a local group-based priori is proposed in the formulation to make a better use of popular block based features (e.g., HOG and GIST). Extensive experiments are conducted on four public databases: Pascal VOC2007, LabelMe, Wikipedia, and NUS-WIDE. We also evaluated the proposed algorithm with unpaired data. By comparing with existing state-of-the-art algorithms, the results show that the proposed algorithm is more robust and achieves the best performance, which outperforms the second best algorithm by about 5% on both the Pascal VOC2007 and NUS-WIDE databases.
关键词	Cross-modal Matching Documents And Images Multimedia Retrieval
WOS标题词	Science & Technology ; Technology
关键词[WOS]	FACE RECOGNITION ; LEAST-SQUARES ; REGRESSION ; SHRINKAGE ; SELECTION
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000351585700009
引用统计	被引频次：185[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8094
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
作者单位	Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Kang, Cuicui,Xiang, Shiming,Liao, Shengcai,et al. Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2015,17(3):370-381.
APA	Kang, Cuicui,Xiang, Shiming,Liao, Shengcai,Xu, Changsheng,&Pan, Chunhong.(2015).Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval.IEEE TRANSACTIONS ON MULTIMEDIA,17(3),370-381.
MLA	Kang, Cuicui,et al."Learning Consistent Feature Representation for Cross-Modal Multimedia Retrieval".IEEE TRANSACTIONS ON MULTIMEDIA 17.3(2015):370-381.

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