Skip to main content

A General Method for Unsupervised Segmentation of Images Using a Multiscale Approach

  • Conference paper
  • First Online:

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

Abstract

We propose a general unsupervised multiscale approach towards image segmentation. The novelty of our method is based on the following points: firstly, it is general in the sense of being independent of the feature extraction process; secondly, it is unsupervised in that the number of classes is not assumed to be known a priori; thirdly, it is flexible as the decomposition sensitivity can be robustly adjusted to produce segmentations into varying number of classes and fourthly, it is robust through the use of the mean shift clustering and Bayesian multiscale processing. Clusters in the joint spatio-feature domain are assumed to be properties of underlying classes, the recovery of which is achieved by the use of the mean shift procedure, a robust non-parametric decomposition method. The subsequent classification procedure consists of Bayesian multiscale processing which models the inherent uncertainty in the joint specification of class and position via a Multiscale Random Field model which forms a Markov Chain in scale. At every scale, the segmentation map and model parameters are determined by sampling from their conditional posterior distributions using Markov Chain Monte Carlo simulations with stochastic relaxation. The method is then applied to perform both colour and texture segmentation. Experimental results show the proposed method performs well even for complicated images.

Keywords

  • Image Segmentation
  • Cluster Centre
  • Coarse Scale
  • Shift Vector
  • Texture 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. Bouman, C., Shapiro, M.: A Multiscale Random Field Model for Bayesian Image Segmentation. IEEE Trans. Image Process. 3(2) (1994) 162–177

    CrossRef  Google Scholar 

  2. Chang, T., Kuo, C.J.: Texture Analysis and Classification with a Tree-Structured Wavelet Transform. IEEE Trans. Image Process. 2(4) (1993) 429–441

    CrossRef  Google Scholar 

  3. Cheng, Y.: Mean Shift, Mode Seeking, and Clustering. IEEE Trans. Pattern Anal. Machine Intell. 17(8) (1993) 770–799

    Google Scholar 

  4. Comaniciu, D., Meer, P.: Distribution Free Decomposition of Multivariate Data. 2nd Intern. Workshop on Statist. Techniques in Patt. Recog., Sydney, Australia. (1998)

    Google Scholar 

  5. Fukunaga, K., Hosteler, L.D.: The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Trans. Info. Theory 21 (1975) 32–40

    MathSciNet  CrossRef  Google Scholar 

  6. Geman, S., Geman, D.: Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images. IEEE Trans. Pattern Anal. Machine Intell. 6(6) (1984) 721–741

    CrossRef  MATH  Google Scholar 

  7. Hastings, W.K.: Monte Carlo Sampling Methods using Markov Chains and their Applications. Biometrika 57 (1970) 97–109

    MathSciNet  CrossRef  MATH  Google Scholar 

  8. Jain, A.K., Farrokhnia, F.: Unsupervised Texture Segmentation using Gabor Filters. Pattern Recognition. 24(12) (1991) 1167–1186

    CrossRef  Google Scholar 

  9. Jeffreys, H.: Theory of Probability. Oxford University Press (1939)

    Google Scholar 

  10. Kingsbury, N.: The Dual-Tree Complex Wavelet Transform: A New Technique for Shift Invariance and Directional Filters. IEEE Dig. Sig. Proc. Workshop, DSP98, Bryce Canyon, paper no. 86. (1998)

    Google Scholar 

  11. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N.: Equation of State Calculations by Fast Computing Machines. Journal of Chem. Phys. 21 (1953) 1087–1092

    CrossRef  MATH  Google Scholar 

  12. Rangen, T., Husoy, J.H.: Multichannel Filtering for Image Texture Segmentation. Opt. Eng. 8 (1994) 2617–2625

    Google Scholar 

  13. Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London. (1986)

    CrossRef  MATH  Google Scholar 

  14. Unser, M., Eden, M.: Non-linear Operators for Improving Texture Segmentation based on Features Extracted by Spatial Filtering. IEEE Trans. Syst., Man and Cyb. 20 (1990) 804–815

    CrossRef  Google Scholar 

  15. Wilson, R., Spann, M.: Image Segmentation and Uncertainty. Research Studies Press Ltd., Letchworth, Hertfordshire, U.K. (1988)

    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

Kam, A.H., Fitzgerald, W.J. (2000). A General Method for Unsupervised Segmentation of Images Using a Multiscale Approach. 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_5

Download citation

  • DOI: https://doi.org/10.1007/3-540-45053-X_5

  • 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.