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Trading Precision for Speed: Localised Similarity Functions

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Image and Video Retrieval (CIVR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3568))

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Abstract

We have generalised a class of similarity measures that are designed to address the problems associated with indexing high-dimensional feature space. The features are stored and indexed component wise. For each dimension we retrieve only those objects close the query point and then apply a local distance function to this subset. Thus we can dramatically reduce the amount of data looked at. We have evaluated these distance measures within a content-based image retrieval (CBIR) framework to determine the trade-off between the percentage of the data retrieved and the precision. Our results show that up to 90% of the data can be ignored whilst maintaining, and in some cases improving, retrieval performance.

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Author information

Authors and Affiliations

  1. Department of Computing, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK

    Peter Howarth & Stefan Rüger

Authors
  1. Peter Howarth
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  2. Stefan Rüger
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, National University of Singapore, Computing 1, 117590, Singapore

    Wee-Kheng Leow

  2. LIACS Media Lab, Leiden University,  

    Michael S. Lew  & Erwin M. Bakker  & 

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

    Tat-Seng Chua

  4. Microsoft Research Asia, 4F, Sigma Center, No.49, Zhichun Road, 100080, Beijing, P.R.China

    Wei-Ying Ma

  5. School of Computing, National University of Singapore, 3 Science Drive 2, 117543, Singapore

    Lekha Chaisorn

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© 2005 Springer-Verlag Berlin Heidelberg

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Howarth, P., Rüger, S. (2005). Trading Precision for Speed: Localised Similarity Functions. In: Leow, WK., Lew, M.S., Chua, TS., Ma, WY., Chaisorn, L., Bakker, E.M. (eds) Image and Video Retrieval. CIVR 2005. Lecture Notes in Computer Science, vol 3568. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526346_45

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  • DOI: https://doi.org/10.1007/11526346_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27858-0

  • Online ISBN: 978-3-540-31678-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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