Skip to main content
SpringerLink
Log in

Maximum Membership Scale Selection

A Classifier Combining Approach to Multi-scale Image Segmentation

  • Conference paper
Multiple Classifier Systems (MCS 2009)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Koenderink, J.: The structure of images. Biological Cybernetics 50(5), 363–370 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  2. Witkin, A.: Scale-space filtering: A new approach to multi-scale description. IEEE International Conference on Acoustics, Speech and Signal Processing 9 (1984)

    Google Scholar 

  3. Chen, C., Lee, J., Sun, Y.: Wavelet transformation for gray-level corner detection. Pattern Recognition 28(6), 853–861 (1995)

    Article  Google Scholar 

  4. Deriche, R., Giraudon, G.: A computational approach for corner and vertex detection. International Journal of Computer Vision 10(2), 101–124 (1993)

    Article  Google Scholar 

  5. ter Haar Romeny, B.: Front-end vision and multi-scale image analysis. Kluwer Academic, Dordrecht (2002)

    MATH  Google Scholar 

  6. Mikolajczyk, K., Schmid, C.: Scale & Affine Invariant Interest Point Detectors. International Journal of Computer Vision 60(1), 63–86 (2004)

    Article  Google Scholar 

  7. Lindeberg, T.: Scale-Space Theory in Computer Vision. Kluwer Academic, Dordrecht (1994)

    Book  MATH  Google Scholar 

  8. Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, London (1999)

    MATH  Google Scholar 

  9. Lindeberg, T.: Feature detection with automatic scale selection. International Journal of Computer Vision 30(2), 79–116 (1998)

    Article  Google Scholar 

  10. Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision 30(2), 117–154 (1998)

    Article  Google Scholar 

  11. Forstner, W., Gulch, E.: A fast operator for detection and precise location of distinct points, corners and centres of circular features. In: Proceedings of the ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, pp. 281–305 (1987)

    Google Scholar 

  12. Rohr, K.: Recognizing corners by fitting parametric models. International Journal of Computer Vision 9(3), 213–230 (1992)

    Article  Google Scholar 

  13. de Bruijne, M.: Shape particle guided tissue classification. In: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (2006)

    Google Scholar 

  14. Folkesson, J., Dam, E., Olsen, O., Pettersen, P., Christiansen, C.: Segmenting articular cartilage automatically using a voxel classification approach. IEEE Transactions on Medical Imaging 26(1), 106–115 (2007)

    Article  Google Scholar 

  15. van Ginneken, B., Stegmann, M., Loog, M.: Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Medical Image Analysis 10(1), 19–40 (2006)

    Article  Google Scholar 

  16. Loog, M., Ginneken, B.: Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification. IEEE Transactions on Medical Imaging 25(5), 602–611 (2006)

    Article  Google Scholar 

  17. Kittler, J.: Combining classifiers: A theoretical framework. Pattern Analysis & Applications 1(1), 18–27 (1998)

    Article  Google Scholar 

  18. Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Hoboken (2004)

    Book  MATH  Google Scholar 

  19. Florack, L.: Image Structure. Kluwer Academic Publishers, Dordrecht (1997)

    Book  Google Scholar 

  20. Koenderink, J., van Doorn, A.: Receptive field families. Biological Cybernetics 63(4), 291–297 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  21. Florack, L., ter Haar Romeny, B., Koenderink, J., Viergever, M.: Scale and the differential structure of images. Image and Vision Computing 10(6), 376–388 (1992)

    Article  MATH  Google Scholar 

  22. Florack, L., Kuijper, A.: The Topological Structure of Scale-Space Images. Journal of Mathematical Imaging and Vision 12(1), 65–79 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  23. Kuijper, A., Florack, L.: The hierarchical structure of images. IEEE Transactions on Image Processing 12(9), 1067–1079 (2003)

    Article  MathSciNet  Google Scholar 

  24. Olsen, O., Florack, L., Kuijper, A. (eds.): DSSCV 2005. LNCS, vol. 3753. Springer, Heidelberg (2005)

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David M. J. Tax
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • 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)

Publish with us

Policies and ethics

Search

Navigation