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CLC number: TP391

On-line Access: 2010-11-04

Received: 2010-09-14

Revision Accepted: 2010-10-09

Crosschecked: 2010-09-26

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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.11 P.903-910

http://doi.org/10.1631/jzus.C1001009


A ranking SVM based fusion model for cross-media meta-search engine


Author(s):  Ya-li Cao, Tie-jun Huang, Yong-hong Tian

Affiliation(s):  Shenzhen Graduate School, Peking University, Shenzhen 518055, China, Institute of Digital Media, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China

Corresponding email(s):   ylcao@jdl.ac.cn

Key Words:  Information fusion, Meta-search, Cross-media, Ranking


Ya-li Cao, Tie-jun Huang, Yong-hong Tian. A ranking SVM based fusion model for cross-media meta-search engine[J]. Journal of Zhejiang University Science C, 2010, 11(11): 903-910.

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author="Ya-li Cao, Tie-jun Huang, Yong-hong Tian",
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1001009


Abstract: 
Recently, we designed a new experimental system MSearch, which is a cross-media meta-search system built on the database of the WikipediaMM task of ImageCLEF 2008. For a meta-search engine, the kernel problem is how to merge the results from multiple member search engines and provide a more effective rank list. This paper deals with a novel fusion model employing supervised learning. Our fusion model employs ranking SVM in training the fusion weight for each member search engine. We assume the fusion weight of each member search engine as a feature of a result document returned by the meta-search engine. For a returned result document, we first build a feature vector to represent the document, and set the value of each feature as the document’s score returned by the corresponding member search engine. Then we construct a training set from the documents returned from the meta-search engine to learn the fusion parameter. Finally, we use the linear fusion model based on the overlap set to merge the results set. Experimental results show that our approach significantly improves the performance of the cross-media meta-search (MSearch) and outperforms many of the existing fusion methods.

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

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