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3D Shape Histograms for Similarity Search and Classification in Spatial Databases

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Advances in Spatial Databases (SSD 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1651))

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Abstract

Classification is one of the basic tasks of data mining in modern database applications including molecular biology, astronomy, mechanical engineering, medical imaging or meteorology. The underlying models have to consider spatial properties such as shape or extension as well as thematic attributes. We introduce 3D shape histograms as an intuitive and powerful similarity model for 3D objects. Particular flexibility is provided by using quadratic form distance functions in order to account for errors of measurement, sampling, and numerical rounding that all may result in small displacements and rotations of shapes. For query processing, a general filter-refinement architecture is employed that efficiently supports similarity search based on quadratic forms. An experimental evaluation in the context of molecular biology demonstrates both, the high classification accuracy of more than 90% and the good performance of the approach.

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Author information

Authors and Affiliations

  1. University of Munich, Oettingenstr. 67, 80538, Munich, Germany

    Mihael Ankerst, Gabi Kastenmüller, Hans-Peter Kriegel & Thomas Seidl

Authors
  1. Mihael Ankerst
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  2. Gabi Kastenmüller
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  3. Hans-Peter Kriegel
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  4. Thomas Seidl
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Editor information

Editors and Affiliations

  1. Praktische Informatik IV, Fernuniversität Hagen, D-58084, Hagen, Germany

    Ralf Hartmut Güting

  2. Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China

    Dimitris Papadias  & Fred Lochovsky  & 

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© 1999 Springer-Verlag Berlin Heidelberg

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Ankerst, M., Kastenmüller, G., Kriegel, HP., Seidl, T. (1999). 3D Shape Histograms for Similarity Search and Classification in Spatial Databases. In: Güting, R.H., Papadias, D., Lochovsky, F. (eds) Advances in Spatial Databases. SSD 1999. Lecture Notes in Computer Science, vol 1651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48482-5_14

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  • DOI: https://doi.org/10.1007/3-540-48482-5_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66247-1

  • Online ISBN: 978-3-540-48482-0

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