Skip to main content

Image Segmentation by Nonparametric Clustering Based on the Kolmogorov-Smirnov Distance

  • Conference paper
  • First Online:

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1843)

Abstract

In this paper we introduce a non-parametric clustering algorithm for 1-dimensional data. The procedure looks for the simplest (i.e. smoothest) density that is still compatible with the data. Compatibility is given a precise meaning in terms of the Kolmogorov-Smirnov statistic. After discussing experimental results for colour segmentation, we outline how this proposed algorithm can be extended to higher dimensions.

Keywords

  • Image Segmentation
  • Gaussian Mixture Model
  • Independent Component Analysis
  • Salient Region
  • Colour Segmentation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. C. Carson, S. Belongie, H. Greenspan, and J. Malik: Region-Based Image Querying. Proc. of CVPR’97 Workshop on Content-Based Access of Image and Video Libraries.

    Google Scholar 

  2. C. Carson, S. Belongie, H. Greenspan, and J. Malik: Blobworld: Image Segmentation using Expectation-Maximization and its application to Image Querying. Submitted to PAMI.

    Google Scholar 

  3. G. Coleman and H.C. Andrews: Image segmentation by clustering. Proc. IEEE 67, 1979, pp. 773–785.

    Google Scholar 

  4. A.P. Dempster, N.M. Laird and D.R. Rubin: Maximum Likelihood from Incomplete Data via the EM Algorithm. J. Royal Statist. Soc.Ser B, 39 (1977), pp. 1–38.

    MathSciNet  MATH  Google Scholar 

  5. R.O. Duda and P.E. Hart: Pattern Classification and Scene Analysis. Wiley 1973.

    Google Scholar 

  6. H. Frigui and R. Krishnapuram: Clustering by Competitive Agglomeration. Pattern Recognition, Vol. 30, No. 7, ppe. 1109–1119, 1997.

    CrossRef  Google Scholar 

  7. K. Fukunaga: Introduction to Statistical Pattern Recognition. Academic Press, 1990.

    Google Scholar 

  8. I.J. Good and R.A. Gaskins: Nonparametric roughness penalties for probability densities. Biometrika 58, 255–77, 1971.

    MathSciNet  CrossRef  MATH  Google Scholar 

  9. A.K. Jain and R.C. Dubes: Algorithms for Clustering Data. Prentice Hall, 1988.

    Google Scholar 

  10. Leonard Kaufman and Peter J. Rousseeuw: Finding Groups in Data: An Introduction to Cluster Analysis. J. Wiley and Sons, 1990.

    Google Scholar 

  11. Brian S. Everitt: Cluster Analysis. Edward Arnold, 1993.

    Google Scholar 

  12. A. Mood, F. Graybill, D. Boes: Introduction to the Theory of Statistics. McGraw-Hill, 1974, 3rd Edition.

    Google Scholar 

  13. E.J. Pauwels, P. Fiddelaers and F. Mindru: Fully Unsupervised Clustering using Center-Surround Receptive Fields with Applications to Colour-Segmentation. Proc. of the 7th. International Conference on Computer Analysis of Images and Patterns. Kiel, Germany, Sept 10–12, 1997.

    Google Scholar 

  14. E.J. Pauwels and G. Frederix: Non-parametric Clustering for Segmentation and Grouping. Proc. of International Workshop on Very Low Bitrate Video Coding VLBV’98, Urbana, IL, Oct. 1998. pp. 133–136.

    Google Scholar 

  15. E.J. Pauwels and G. Frederix: Finding Salient Regions in Images. Computer Vision and Image Understanding, Vol. 75, Nos 1/2, July/August 1999, pp. 73–85.

    CrossRef  Google Scholar 

  16. J. Shi and J. Malik: Normalized Cuts and Image Segmentation. Proc. IEEE Conf. oon Comp. Vision and Pattern Recognition, San Juan, Puerto Rico, Jun

    Google Scholar 

  17. W. Press, B. Flannery, S. Teukolsky, W. Vetterling: Numerical Recipes. Cambridge University Press, 1989.

    MATH  Google Scholar 

  18. J.R. Thompson and R.A. Tapia: Nonparametric Function Estimation, Modeling and Simulation. SIAM, 1990.

    Google Scholar 

  19. John L. Troutman: Variational Calculus with Elementary Convexity. UTM, Springer-Verlag, 1983.

    Google Scholar 

  20. V.N. Vapnik: The Nature of Statistical Learning Theory. Springer, 1995.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pauwels, E.J., Frederix, G. (2000). Image Segmentation by Nonparametric Clustering Based on the Kolmogorov-Smirnov Distance. In: Vernon, D. (eds) Computer Vision — ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol 1843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45053-X_6

Download citation

  • DOI: https://doi.org/10.1007/3-540-45053-X_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67686-7

  • Online ISBN: 978-3-540-45053-5

  • eBook Packages: Springer Book Archive

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.