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Context-Sensitive Ranking for Effective Image Retrieval

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Advances in Multimedia Modeling (MMM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4351))

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Abstract

Over many years, almost all research work in the content-based image retrieval (CBIR) has used Minkowski metric (or L p -norm) to measure similarity between images. However, those functions cannot adequately capture the nonlinear relationships in contextual information given by image datasets. In this paper, we present a new similarity measure reflecting the nonlinearity of contextual information. Moreover, we propose a new similarity ranking algorithm based on this similarity measure for effective CBIR. Our algorithm yields superior experimental results on real image database and demonstrates its effectiveness.

This work was supported by grant No. B1220-0501-0233 from the University Fundamental Research Program of the Ministry of Information & Communication in Republic of Korea.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Seoul National University of Technology, Seoul, 139-743, South Korea

    Guang-Ho Cha

Authors
  1. Guang-Ho Cha
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Editor information

Editors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Block N4, Nanyang Avenue, 639798, Singapore

    Tat-Jen Cham  & Deepu Rajan  & 

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

    Jianfei Cai

  3. IBM T.J. Watson Research Center, Yorktown Heights, P.O. Box 704, 10598, New York, USA

    Chitra Dorai

  4. National University of Singapore, 3 Science Dr, 117543, Singapore

    Tat-Seng Chua

  5. Center for Multimedia and Network Technology, School of Computer Enginnering, Nanyang Technological University, 639798, Singapore

    Liang-Tien Chia

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© 2006 Springer-Verlag Berlin Heidelberg

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Cha, GH. (2006). Context-Sensitive Ranking for Effective Image Retrieval. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69423-6_34

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  • DOI: https://doi.org/10.1007/978-3-540-69423-6_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69421-2

  • Online ISBN: 978-3-540-69423-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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