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Building Multi-Modal Relational Graphs for Multimedia Retrieval

Building Multi-Modal Relational Graphs for Multimedia Retrieval

Jyh-Ren Shieh, Ching-Yung Lin, Shun-Xuan Wang, Ja-Ling Wu
Copyright: © 2011 |Volume: 2 |Issue: 2 |Pages: 23
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781613508527|DOI: 10.4018/jmdem.2011040102
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MLA

Shieh, Jyh-Ren, et al. "Building Multi-Modal Relational Graphs for Multimedia Retrieval." IJMDEM vol.2, no.2 2011: pp.19-41. http://doi.org/10.4018/jmdem.2011040102

APA

Shieh, J., Lin, C., Wang, S., & Wu, J. (2011). Building Multi-Modal Relational Graphs for Multimedia Retrieval. International Journal of Multimedia Data Engineering and Management (IJMDEM), 2(2), 19-41. http://doi.org/10.4018/jmdem.2011040102

Chicago

Shieh, Jyh-Ren, et al. "Building Multi-Modal Relational Graphs for Multimedia Retrieval," International Journal of Multimedia Data Engineering and Management (IJMDEM) 2, no.2: 19-41. http://doi.org/10.4018/jmdem.2011040102

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Abstract

The abundance of Web 2.0 social media in various media formats calls for integration that takes into account tags associated with these resources. The authors present a new approach to multi-modal media search, based on novel related-tag graphs, in which a query is a resource in one modality, such as an image, and the results are semantically similar resources in various modalities, for instance text and video. Thus the use of resource tagging enables the use of multi-modal results and multi-modal queries, a marked departure from the traditional text-based search paradigm. Tag relation graphs are built based on multi-partite networks of existing Web 2.0 social media such as Flickr and Wikipedia. These multi-partite linkage networks (contributor-tag, tag-category, and tag-tag) are extracted from Wikipedia to construct relational tag graphs. In fusing these networks, the authors propose incorporating contributor-category networks to model contributor’s specialization; it is shown that this step significantly enhances the accuracy of the inferred relatedness of the term-semantic graphs. Experiments based on 200 TREC-5 ad-hoc topics show that the algorithms outperform existing approaches. In addition, user studies demonstrate the superiority of this visualization system and its usefulness in the real world.

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