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Comprehensive query-dependent fusion using regression-on-folksonomies: a case study of multimodal music search
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International Multimedia Conference archive
Proceedings of the seventeen ACM international conference on Multimedia table of contents
Beijing, China
SESSION: Content track C5: audio and music table of contents
Pages 213-222  
Year of Publication: 2009
ISBN:978-1-60558-608-3
Authors
Bingjun Zhang  National University of Singapore, Singapore
Qiaoliang Xiang  National University of Singapore, Singapore
Huanhuan Lu  National University of Singapore, Singapore
Jialie Shen  Singapore Management University, Singapore
Ye Wang  National University of Singapore, Singapore
Sponsor
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
Publisher
ACM  New York, NY, USA
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ABSTRACT

The combination of heterogeneous knowledge sources has been widely regarded as an effective approach to boost retrieval accuracy in many information retrieval domains. While various technologies have been recently developed for information retrieval, multimodal music search has not kept pace with the enormous growth of data on the Internet. In this paper, we study the problem of integrating multiple online information sources to conduct effective query dependent fusion (QDF) of multiple search experts for music retrieval. We have developed a novel framework to construct a knowledge space of users' information need from online folksonomy data. With this innovation, a large number of comprehensive queries can be automatically constructed to train a better generalized QDF system against unseen user queries. In addition, our framework models QDF problem by regression of the optimal combination strategy on a query. Distinguished from the previous approaches, the regression model of QDF (RQDF) offers superior modeling capability with less constraints and more efficient computation. To validate our approach, a large scale test collection has been collected from different online sources, such as Last.fm, Wikipedia, and YouTube. All test data will be released to the public for better research synergy in multimodal music search. Our performance study indicates that the accuracy, efficiency, and robustness of the multimodal music search can be improved significantly by the proposed folksonomy-RQDF approach. In addition, since no human involvement is required to collect training examples, our approach offers great feasibility and practicality in system development.


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