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A New Similarity Measure for Random Signatures: Perceptually Modified Hausdorff Distance

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4179))

Abstract

In most content-based image retrieval systems, the low level visual features such as color, texture and region play an important role. Variety of dissimilarity measures were introduced for an uniform quantization of visual features, or a histogram. However, a cluster-based representation, or a signature, has proven to be more compact and theoretically sound for the accuracy and robustness than a histogram. Despite of these advantages, so far, only a few dissimilarity measures have been proposed. In this paper, we present a novel dissimilarity measure for a random signature, Perceptually Modified Hausdorff Distance (PMHD), based on Hausdorff distance. In order to demonstrate the performance of the PMHD, we retrieve relevant images for some queries on real image database by using only color information. The precision vs. recall results show that the proposed dissimilarity measure generally outperforms all other dissimilarity measures on an unmodified commercial image database.

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Author information

Authors and Affiliations

  1. School of Electrical Eng., ASRI, Seoul National University, 151-600, Seoul, Korea

    Bo Gun Park, Kyoung Mu Lee & Sang Uk Lee

Authors
  1. Bo Gun Park
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  2. Kyoung Mu Lee
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  3. Sang Uk Lee
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Editor information

Editors and Affiliations

  1. DGA/D4S/MRIS, 94114, Arcueil, France

    Jacques Blanc-Talon

  2. Ghent University, 9000, Gent, Belgium

    Wilfried Philips

  3. CSIRO ICT Centre, 1710, Sydney, NSW, Australia

    Dan Popescu

  4. University of Antwerp, 2610, Wilrijk, Belgium

    Paul Scheunders

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© 2006 Springer-Verlag Berlin Heidelberg

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Park, B.G., Lee, K.M., Lee, S.U. (2006). A New Similarity Measure for Random Signatures: Perceptually Modified Hausdorff Distance. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_90

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  • DOI: https://doi.org/10.1007/11864349_90

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44630-9

  • Online ISBN: 978-3-540-44632-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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