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Maximum Membership Scale Selection

A Classifier Combining Approach to Multi-scale Image Segmentation

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Multiple Classifier Systems (MCS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5519))

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Abstract

The use of multi-scale features is explored in the setting of supervised image segmentation by means of pixel classification. More specifically, we consider an interesting link between so-called scale selection and the maximum combination rule from pattern recognition. The parallel with scale selection is drawn further and a multi-scale segmentation method is introduced that relies on a per-scale classification followed by an over-scale fusion of these outcomes. A limited number of experiments is presented to provide some further understanding of the technique proposed.

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands

    Marco Loog, Yan Li & David M. J. Tax

Authors
  1. Marco Loog
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  2. Yan Li
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  3. David M. J. Tax
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Editor information

Editors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Iceland, Hjardarhagi 2-6, 107, Reykjavik, Iceland

    Jón Atli Benediktsson

  2. Speech and Signal Processing, Guildford, University of Surrey, Centre for Vision, GU2 7XH, Surrey, United Kingdom

    Josef Kittler

  3. Department of Electrical and Electronic Engineering, Piazza d’Armi, University of Cagliari, 09123, Cagliari, Italy

    Fabio Roli

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© 2009 Springer-Verlag Berlin Heidelberg

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Loog, M., Li, Y., Tax, D.M.J. (2009). Maximum Membership Scale Selection. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_47

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  • DOI: https://doi.org/10.1007/978-3-642-02326-2_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02325-5

  • Online ISBN: 978-3-642-02326-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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