Advertisement

Hierarchical Regions for Image Segmentation

  • Slawo Wesolkowski
  • Paul Fieguth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

Image segmentation is one of the key problems in computer vision. Gibbs Random Fields (GRFs), which produce elegant models, but which have very poor computational speed have been widely applied to image segmentation. In this paper, we propose a hierarchical region-based approach to the GRF. In contrast to block-based hierarchies usually constructed for GRFs, the irregular region-based approach is a far more natural model in segmenting real images. By deliberately oversegmenting at the finer scales, the method proceeds conservatively by avoiding the construction of regions which straddle a region boundary. In addition to the expected benefit of computational speed and preserved modelling elegance, our approach does not require a stopping criterion, common in iterated segmentation methods, since the hierarchy seeks the unique minimum of the original GRF model.

Keywords

Image Segmentation Unique Minimum Region Competition Color Segmentation Irregular Grid 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Angulo, J., Serra, J.: Color segmentation by ordered mergings. In: IEEE ICIP, Barcelona, September 2003, vol. 2, pp. 125–128 (2003)Google Scholar
  2. 2.
    Barbu, A., Zhu, S.C.: Graph Partition by Swendsen-Wang Cut. IEEE Trans. on Pattern Analysis and Machine Intelligence (2004) (under review)Google Scholar
  3. 3.
    Kato, Z., Berthod, M., Zeroubia, J.: A Hierarchical Markov Random Field Model and Multitemperature Annealing for Parallel Image Classification. Graphical Models and Image Processing 58(1), 18–37 (1996)CrossRefGoogle Scholar
  4. 4.
    Fieguth, P., Wesolkowski, S.: Highlight and Shading Invariant Color Image Segmentation Using Simulated Annealing. In: Energy Minimization Methods in Computer Vision and Pattern Recognition III, Sophia-Antipolis, France, September 2001, pp. 314–327 (2001)Google Scholar
  5. 5.
    Geman, S., Geman, D.: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans-PAMI 6(6) (1984)Google Scholar
  6. 6.
    Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, vol. 1. Addison- Welsey, Reading (1992)Google Scholar
  7. 7.
    Li, S.Z.: Markov Random Field Modelling in Image Analysis. Springer, Japan (2001)Google Scholar
  8. 8.
    Lucchese, L., Mitra, S.K.: Color Image Segmentation: A State-of-the-Art Survey. In: Proc. of the Indian National Science Academy (INSA-A), New Delhi, India, March 2001, vol. 67 A(2), pp. 207–221 (2001)Google Scholar
  9. 9.
    Swendsen, R.H., Wang, J.S.: Nonuniversal critical dynamics in Monte Carlo simulations. Physical Review Letters 58(2), 86–88 (1987)CrossRefGoogle Scholar
  10. 10.
    Tremeau, A., Borel, N.: A Region Growing and Merging Algorithm to Color Segmentation. Pattern Recognition 30(7), 1191–1203 (1997)CrossRefGoogle Scholar
  11. 11.
    Winkler, G.: Image Analysis, Random Fields and Dynamic Monte Carlo Methods. Springer, Berlin (1995)zbMATHGoogle Scholar
  12. 12.
    Zhu, S.C., Yuille, A.: Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(9), 884–900 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Slawo Wesolkowski
    • 1
  • Paul Fieguth
    • 1
  1. 1.Systems Design EngineeringUniversity of WaterlooWaterlooCanada

Personalised recommendations