Ranking with local regression and global alignment for cross media retrieval

Yang, Yi, Xu, Dong, Nie, Feiping, Luo, Jiebo and Zhuang, Yueting (2009). Ranking with local regression and global alignment for cross media retrieval. In: Proceedings of the 17th ACM international conference on Multimedia. 17th ACM International Conference on Multimedia, Beijing, China, (175-184). 19-24 October 2009. doi:10.1145/1631272.1631298

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Author Yang, Yi
Xu, Dong
Nie, Feiping
Luo, Jiebo
Zhuang, Yueting
Title of paper Ranking with local regression and global alignment for cross media retrieval
Language of Title eng
Conference name 17th ACM International Conference on Multimedia
Conference location Beijing, China
Conference dates 19-24 October 2009
Proceedings title Proceedings of the 17th ACM international conference on Multimedia
Language of Proceedings Title eng
Language of Journal Name eng
Place of Publication New York, United States
Publisher Association for Computing Machinery
Publication Year 2009
Sub-type Fully published paper
DOI 10.1145/1631272.1631298
ISBN 9781605586083
Start page 175
End page 184
Total pages 10
Language eng
Abstract/Summary Rich multimedia content including images, audio and text are frequently used to describe the same semantics in E-Learning and Ebusiness web pages, instructive slides, multimedia cyclopedias, and so on. In this paper, we present a framework for cross-media retrieval, where the query example and the retrieved result(s) can be of different media types. We first construct Multimedia Correlation Space (MMCS) by exploring the semantic correlation of different multimedia modalities, during which multimedia content and co-occurrence information is utilized. We propose a novel ranking algorithm, namely ranking with Local Regression and Global Alignment (LRGA), which learns a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking values of its neighboring points. We propose a unified objective function to globally align the local models from all the data points so that an optimal ranking value can be assigned to each data point. LRGA is insensitive to parameters, making it particularly suitable for data ranking. A relevance feedback algorithm is proposed to improve the retrieval performance. Comprehensive experiments have demonstrated the effectiveness of our methods.
Keyword Content-based multimedia retrieval
Cross-media retrieval
Ranking algorithm
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

 
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