A Graph-Based Hierarchical Image Segmentation Method Based on a Statistical Merging Predicate

  • Silvio Jamil F. Guimarães
  • Zenilton K. G. PatrocínioJr.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


Hierarchical image segmentation provides a set of image segmentations at different detail levels in which coaser details levels can be produced by simple merges of regions from segmentations at finer detail levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy. In addition, for image segmentation, the tuning of the parameters can be difficult. In this work, we propose a hierarchical graph-based image segmentation relying on a statistical region merging. Furthermore, we study how the inclusion of hierarchical property have influenced the computation of quality measures in the original method. Quantitative and qualitative assessments of the method on two image databases show efficiency and ease of use of our method.


hierarchical segmentation vertex-edge-weighted graph statistical region merging predicate 


  1. 1.
    Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (June 2007)Google Scholar
  2. 2.
    Alpert, S., Galun, M., Brandt, A., Basri, R.: Image segmentation by probabilistic bottom-up aggregation and cue integration. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 315–327 (2012)CrossRefGoogle Scholar
  3. 3.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. PAMI 33, 898–916 (2011)CrossRefGoogle Scholar
  4. 4.
    Cousty, J., Najman, L.: Incremental algorithm for hierarchical minimum spanning forests and saliency of watershed cuts. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 272–283. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59, 167–181 (2004), CrossRefGoogle Scholar
  6. 6.
    Guigues, L., Cocquerez, J.P., Men, H.L.: Scale-sets image analysis. IJCV 68(3), 289–317 (2006), CrossRefGoogle Scholar
  7. 7.
    Guimarães, S.J.F., Cousty, J., Kenmochi, Y., Najman, L.: An efficient hierarchical graph based image segmentation. CoRR abs/1206.2807 (2012)Google Scholar
  8. 8.
    Guimarães, S.J.F., Cousty, J., Kenmochi, Y., Najman, L.: A hierarchical image segmentation algorithm based on an observation scale. In: Gimel’farb, G., Hancock, E., Imiya, A., Kuijper, A., Kudo, M., Omachi, S., Windeatt, T., Yamada, K. (eds.) SSPR & SPR 2012. LNCS, vol. 7626, pp. 116–125. Springer, Heidelberg (2012)Google Scholar
  9. 9.
    Haxhimusa, Y., Kropatsch, W.: Segmentation graph hierarchies. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 343–351. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Morris, O., Lee, M.J., Constantinides, A.: Graph theory for image analysis: an approach based on the shortest spanning tree. Communications, Radar and Signal Processing IEE Proceedings F 133(2), 146–152 (1986)CrossRefGoogle Scholar
  11. 11.
    Najman, L.: On the equivalence between hierarchical segmentations and ultrametric watersheds. JMIV 40, 231–247 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Nock, R., Nielsen, F.: Statistical region merging. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11), 1452–1458 (2004)CrossRefGoogle Scholar
  13. 13.
    Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 20, 68–86 (1971), CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Silvio Jamil F. Guimarães
    • 1
  • Zenilton K. G. PatrocínioJr.
    • 1
  1. 1.Audio-Visual Information Proc. Lab. (VIPLAB) Computer Science DepartmentICEI - PUC MinasBrazil

Personalised recommendations