Semi-supervised Probabilistic Relaxation for Image Segmentation

  • Adolfo Martínez-Usó
  • Filiberto Pla
  • José M. Sotoca
  • Henry Anaya-Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)


In this paper, a semi-supervised approach based on probabilistic relaxation theory is presented. Focused on image segmentation, the presented technique combines two desirable properties; a very small number of labelled samples is needed and the assignment of labels is consistently performed according to our contextual information constraints. Our proposal has been tested on medical images from a dermatology application with quite promising preliminary results. Not only the unsupervised accuracies have been improved as expected but similar accuracies to other semi-supervised approach have been obtained using a considerably reduced number of labelled samples. Results have been also compared with other powerful and well-known unsupervised image segmentation techniques, improving significantly their results.


Semi-supervised Image segmentation Probabilistic Relaxation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bouman, C.A.: Cluster: An unsupervised algorithm for modeling Gaussian mixtures (April 1997),
  2. 2.
    Chapelle, O., Scholkopf, B., Zien, A. (eds.): Semi-supervised Learning. MIT Press, Cambridge (2006)Google Scholar
  3. 3.
    Christmas, W.J.: Structural matching in computer vision using probabilistic reasoning. PhD thesis, CVSSP, University of Surrey (1995)Google Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Robust analysis of feature spaces: Color image segmentation. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 750–755 (1997)Google Scholar
  5. 5.
    Diplaros, A., Vlassis, N., Gevers, T.: A spatially constrained generative model and an em algorithm for image segmentation. IEEE Trans. on Neural Networks 18(3), 798–808 (2007)CrossRefGoogle Scholar
  6. 6.
    Faber, P.: A theoretical framework for relaxation processes in pattern recognition: Application to robust nonparametric contour generalization. IEEE Trans. on PAMI 25(8), 1021–1027 (2003)CrossRefGoogle Scholar
  7. 7.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)CrossRefGoogle Scholar
  8. 8.
    Haralick, R.M.: Decision making in context. IEEE Trans. on PAMI 5(4), 417–428 (1983)CrossRefzbMATHGoogle Scholar
  9. 9.
    Hummel, R.A., Zucker, S.W.: On the foundations of relaxation labeling processes. IEEE Trans. on PAMI 5(3), 267–287 (1983)CrossRefzbMATHGoogle Scholar
  10. 10.
    Martinez-Uso, A., Pla, F., Sotoca, J.M.: A semi-supervised gaussian mixture model for image segmentation. In: ICPR, pp. 2941–2944 (2010)Google Scholar
  11. 11.
    Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. on PAMI 26(11), 1452–1458 (2004)CrossRefGoogle Scholar
  12. 12.
    Wang, H.F., Hancock, E.R.: Probabilistic relaxation using the heat equation. In: ICPR, vol. 2, pp. 666–669 (2006)Google Scholar
  13. 13.
    Kittler, J., Christmas, W.J., Petrou, M.: Structural matching in computer vision using probabilistic relaxation. IEEE Trans. on PAMI 17(8), 749–764 (1995)CrossRefGoogle Scholar
  14. 14.
    Zhu, X.: Semi-supervised learning literature survey. Technical Survey 1530, Computer Sciences, University of Wisconsin-Madison (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Adolfo Martínez-Usó
    • 1
  • Filiberto Pla
    • 1
  • José M. Sotoca
    • 1
  • Henry Anaya-Sánchez
    • 1
  1. 1.Institute of New Imaging Technologies - Dept. of Computer Languages and SystemsUniversitat Jaume ICastellónSpain

Personalised recommendations