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Non-metric Similarity Ranking for Image Retrieval

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Database and Expert Systems Applications (DEXA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4080))

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Abstract

Over many years, almost all research work in the content-based image retrieval has used Minkowski distance (or L p -norm) to measure similarity between images. However such functions cannot adequately capture the aspects of the characteristics of the human visual system. In this paper, we present a new similarity measure reflecting the nonlinearity of human perception. Based on this measure, we develop a similarity ranking algorithm for effective image retrieval. This algorithm exploits the inherent cluster structure revealed by an image dataset. Our method yields encouraging experimental results on a 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 Computing, National University of Singapore,  

    Stéphane Bressan

  2. University of Linz, Altenbergerstraße 69, 4040, Linz, Austria

    Josef Küng  & Roland Wagner  & 

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© 2006 Springer-Verlag Berlin Heidelberg

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Cha, GH. (2006). Non-metric Similarity Ranking for Image Retrieval. In: Bressan, S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2006. Lecture Notes in Computer Science, vol 4080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11827405_83

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  • DOI: https://doi.org/10.1007/11827405_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37871-6

  • Online ISBN: 978-3-540-37872-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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