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CVPIC Colour/Shape Histograms for Compressed Domain Image Retrieval

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Pattern Recognition (DAGM 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3175))

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Abstract

Compressed domain image retrieval allows image indexing to be performed directly on the compressed data without the need of decoding. This approach hence provides a significant gain in terms of speed and also eliminates the need to store feature indices. In this paper we introduce a compressed domain image retrieval technique based on the Colour Visual Pattern Image Coding (CVPIC) compression algorithm. CVPIC represents an image coding technique where the compressed form is directly meaningful. Data that is readily available includes information on colour and edge (shape) descriptors of image subblocks. It is this information that is utilised by calculating a combined colour and shape histogram. Experimental results on the UCID dataset show this novel approach to be both efficient and effective, outperforming methods such as colour histograms, colour coherence vectors, and colour correlograms.

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Author information

Authors and Affiliations

  1. School of Computing and Technology, The Nottingham Trent University, Nottingham, United Kingdom

    Gerald Schaefer

Authors
  1. Gerald Schaefer
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Editor information

Editors and Affiliations

  1. Max Planck Institute for Biological Cybernetics,, Spemannstr. 38, D-72076, Tübingen, Germany

    Carl Edward Rasmussen

  2. Max-Planck-Institute for Biological Cybernetics,, Spemannstr. 38, 72076, Tübingen, Germany

    Heinrich H. Bülthoff

  3. MPI for Biological Cybernetics, Spemannstr. 38, 72076, Tübingen, Germany

    Bernhard Schölkopf

  4. School of Psychology, University of Wales, Bangor, UK

    Martin A. Giese

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© 2004 Springer-Verlag Berlin Heidelberg

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Schaefer, G. (2004). CVPIC Colour/Shape Histograms for Compressed Domain Image Retrieval. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_52

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  • DOI: https://doi.org/10.1007/978-3-540-28649-3_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22945-2

  • Online ISBN: 978-3-540-28649-3

  • eBook Packages: Springer Book Archive

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