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CLC number: TP37; TP391

On-line Access: 2008-01-10

Received: 2007-04-11

Revision Accepted: 2007-08-12

Crosschecked: 0000-00-00

Cited: 2

Clicked: 6064

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.2 P.241-249


Understanding visual-auditory correlation from heterogeneous features for cross-media retrieval

Author(s):  Hong ZHANG, Yan-yun WANG, Hong PAN, Fei WU

Affiliation(s):  College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, China; more

Corresponding email(s):   zhanghong_zju@yahoo.com.cn

Key Words:  Heterogeneity, Cross-media retrieval, Subspace optimization, Dynamic correlation update

Hong ZHANG, Yan-yun WANG, Hong PAN, Fei WU. Understanding visual-auditory correlation from heterogeneous features for cross-media retrieval[J]. Journal of Zhejiang University Science A, 2008, 9(2): 241-249.

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author="Hong ZHANG, Yan-yun WANG, Hong PAN, Fei WU",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%T Understanding visual-auditory correlation from heterogeneous features for cross-media retrieval
%A Yan-yun WANG
%A Hong PAN
%A Fei WU
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A071191

T1 - Understanding visual-auditory correlation from heterogeneous features for cross-media retrieval
A1 - Hong ZHANG
A1 - Yan-yun WANG
A1 - Hong PAN
A1 - Fei WU
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 2
SP - 241
EP - 249
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A071191

cross-media retrieval is an interesting research topic, which seeks to remove the barriers among different modalities. To enable cross-media retrieval, it is needed to find the correlation measures between heterogeneous low-level features and to judge the semantic similarity. This paper presents a novel approach to learn cross-media correlation between visual features and auditory features for image-audio retrieval. A semi-supervised correlation preserving mapping (SSCPM) method is described to construct the isomorphic SSCPM subspace where canonical correlations between the original visual and auditory features are further preserved. subspace optimization algorithm is proposed to improve the local image cluster and audio cluster quality in an interactive way. A unique relevance feedback strategy is developed to update the knowledge of cross-media correlation by learning from user behaviors, so retrieval performance is enhanced in a progressive manner. Experimental results show that the performance of our approach is effective.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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