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Hierarchical Image Segmentation Relying on a Likelihood Ratio Test

  • Silvio Jamil F. Guimarães
  • Zenilton Kleber G. do PatrocínioJr.
  • Yukiko Kenmochi
  • Jean Cousty
  • Laurent Najman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)

Abstract

Hierarchical image segmentation provides a set of image segmentations at different detail levels in which coarser details levels can be produced by simple merges of regions from segmentations at finer detail levels. However, many image segmentation algorithms relying on similarity measures lead to no hierarchy. One of interesting similarity measures is a likelihood ratio, in which each region is modelled by a Gaussian distribution to approximate the cue distributions. In this work, we propose a hierarchical graph-based image segmentation inspired by this likelihood ratio test. 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 three well known image databases show efficiency.

Keywords

Hierarchical image segmentation Graph-based method Statistical properties 

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References

  1. 1.
    Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: CVPR, June 2007Google Scholar
  2. 2.
    Alpert, S., Galun, M., Brandt, A., Basri, R.: Image segmentation by probabilistic bottom-up aggregation and cue integration. PAMI 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.
    Beucher, S.: Watershed, hierarchical segmentation and waterfall algorithm. In: Proceedings of the 2nd International Symposium on Mathematical Morphology and Its Applications to Image Processing, ISMM 1994, Fontainebleau, France, September 1994, pp. 69–76 (1994)Google Scholar
  5. 5.
    Calderero, F., Marques, F.: Region merging techniques using information theory statistical measures. Trans. Img. Proc. 19(6), 1567–1586 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Cousty, J., Najman, L.: Morphological floodings and optimal cuts in hierarchies. In: ICIP, pp. 4462–4466 (2014)Google Scholar
  8. 8.
    Cousty, J., Najman, L., Kenmochi, Y., Guimarães, S.: New characterizations of minimum spanning trees and of saliency maps based on quasi-flat zones. In: Benediktsson, J.A., Chanussot, J., Najman, L., Talbot, H. (eds.) ISMM 2015. LNCS, vol. 9082, pp. 205–216. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  9. 9.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59, 167–181 (2004)CrossRefGoogle Scholar
  10. 10.
    Guigues, L., Cocquerez, J.P., Men, H.L.: Scale-sets image analysis. IJCV 68(3), 289–317 (2006)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Guimarães, S.J.F., Patrocínio Jr., Z.K.G.: A graph-based hierarchical image segmentation method based on a statistical merging predicate. In: Petrosino, A. (ed.) ICIAP 2013, Part I. LNCS, vol. 8156, pp. 11–20. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  13. 13.
    Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI 26(5), 530–549 (2004)CrossRefGoogle Scholar
  14. 14.
    Morris, O., Lee, M.J., Constantinides, A.: Graph theory for image analysis: an approach based on the shortest spanning tree. IEE Proceedings F (Communications, Radar and Signal Processing) 133(2), 146–152 (1986)CrossRefGoogle Scholar
  15. 15.
    Najman, L.: On the equivalence between hierarchical segmentations and ultrametric watersheds. JMIV 40, 231–247 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Nock, R., Nielsen, F.: Statistical region merging. PAMI 26(11), 1452–1458 (2004)CrossRefGoogle Scholar
  17. 17.
    Peng, B., Zhang, D., Zhang, D.: Automatic image segmentation by dynamic region merging. IEEE Trans. on Image Processing 20(12), 3592–3605 (2011)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Rother, C., Kolmogorov, V., Blake, A.: “grabcut”: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)CrossRefGoogle Scholar
  19. 19.
    Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 20, 68–86 (1971)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Silvio Jamil F. Guimarães
    • 1
    • 2
  • Zenilton Kleber G. do PatrocínioJr.
    • 1
  • Yukiko Kenmochi
    • 2
  • Jean Cousty
    • 2
  • Laurent Najman
    • 2
  1. 1.PUC Minas - ICEI - DCC - VIPLABBelo HorizonteBrazil
  2. 2.Université Paris-Est, LIGM, ESIEE Paris - CNRSChamps-sur-MarneFrance

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