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Social Media Modeling and Computing pp 47–70Cite as

Social Image Tag Ranking by Two-View Learning

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Abstract

Tags play a central role in text-based social image retrieval and browsing. However, the tags annotated by web users could be noisy, irrelevant, and often incomplete for describing the image contents, which may severely deteriorate the performance of text-based image retrieval models. In order to solve this problem, researchers have proposed techniques to rank the annotated tags of a social image according to their relevance to the visual content of the image. In this paper, we aim to overcome the challenge of social image tag ranking for a corpus of social images with rich user-generated tags by proposing a novel two-view learning approach. It can effectively exploit both textual and visual contents of social images to discover the complicated relationship between tags and images. Unlike the conventional learning approaches that usually assumes some parametric models, our method is completely data-driven and makes no assumption about the underlying models, making the proposed solution practically more effective. We formulate our method as an optimization task and present an efficient algorithm to solve it. To evaluate the efficacy of our method, we conducted an extensive set of experiments by applying our technique to both text-based social image retrieval and automatic image annotation tasks. Our empirical results showed that the proposed method can be more effective than the conventional approaches.

This book chapter is an extended version of the paper [35], which will appear at the fourth ACM Conference on Web Search and Data Mining (WSDM), Hong Kong, 2011.

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Notes

  1. 1.

    http://www.flickr.com/.

  2. 2.

    http://www.flickr.com/.

  3. 3.

    http://glaros.dtc.umn.edu/gkhome/views/cluto/.

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Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Jinfeng Zhuang & Steven C. H. Hoi

Authors
  1. Jinfeng Zhuang
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  2. Steven C. H. Hoi
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Corresponding author

Correspondence to Jinfeng Zhuang .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Steven C.H. Hoi

  2. Kodak Research Laboratories, Lake Avenue 1999, Rochester, 14650, New York, USA

    Jiebo Luo

  3. Media Informatics and Multimedia Systems, University of Oldenburg, Escherweg 2, Oldenburg, 26121, Germany

    Susanne Boll

  4. School of Computer Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Dong Xu

  5. Dept. Computer Science and Engineering, Michigan State University, Engineering Building 3115, East Lansing, 48824, Michigan, USA

    Rong Jin

  6. Dept. Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong/PR China

    Irwin King

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© 2011 Springer-Verlag London Limited

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Zhuang, J., Hoi, S.C.H. (2011). Social Image Tag Ranking by Two-View Learning. In: Hoi, S., Luo, J., Boll, S., Xu, D., Jin, R., King, I. (eds) Social Media Modeling and Computing. Springer, London. https://doi.org/10.1007/978-0-85729-436-4_3

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  • DOI: https://doi.org/10.1007/978-0-85729-436-4_3

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-435-7

  • Online ISBN: 978-0-85729-436-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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