Fast Unsupervised Texture Segmentation Using Active Contours Model Driven by Bhattacharyya Gradient Flow

  • Foued Derraz
  • Abdelmalik Taleb-Ahmed
  • Antonio Pinti
  • Laurent Peyrodie
  • Nacim Betrouni
  • Azzeddine Chikh
  • Fethi Bereksi-Reguig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

We present a new unsupervised segmentation based active contours model and texture descriptor. The proposed texture descriptor intrinsically describes the geometry of textural regions using the shape operator defined in Beltrami framework. We use Bhattacharyya distance to discriminate textures by maximizing distance between the probability density functions which leads to distinguish textural objects of interest and background. We propose a fast Bregman split implementation of our segmentation algorithm based on the dual formulation of the Total Variation norm. Finally, we show results on some challenging images to illustrate segmentations that are possible.

Keywords

Active contours texture descriptor bhattacharyya distance total variation Bregman split algorithm 

References

  1. 1.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. IJCV 22(1), 61–97 (1997)MATHCrossRefGoogle Scholar
  2. 2.
    Sochen, N., Kimmel, R., Malladi, R.: A general framework for low level vision. IEEE TIP 7(3), 310–318 (1996)MathSciNetGoogle Scholar
  3. 3.
    Sagiv, C., Sochen, N., Zeevi, Y.: Integrated active contours for texture segmentation. IEEE TIP 15(6), 1633–1646 (2006)Google Scholar
  4. 4.
    Aujol, J.-F., Gilboa, G., Chan, T., Osher, S.: Structure-Texture Image Decomposition-Modeling, Algorithms, and Parameter Selection. IJCV 67(1), 111–136 (2006)CrossRefGoogle Scholar
  5. 5.
    Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J., Osher, S.: Fast Global Minimization of the Active Contour/Snake Model. JMIV 28(2), 51–167 (2007)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Mi, A.S., Iakovidis, D.K., Maroulis, D.: LBP-guided active contours. Pattern Recognition Letters 29(9), 1404–1415 (2008)CrossRefGoogle Scholar
  7. 7.
    Chan, T., Vese, L.: Active Contours Without Edges. IEEE TIP 10(2), 266–277 (2001)MATHGoogle Scholar
  8. 8.
    Delfour, M., Zolésio, J.: Shapes and Geometries: Analysis, Differential Calculus, and Optimization. Advances in Design and Control, SIAM (2001)Google Scholar
  9. 9.
    Freedman, D., Zhang, T.: Active contours for tracking distributions. IEEE TIP 13(4), 518–526 (2004)Google Scholar
  10. 10.
    Herbulot, A., Jehan-Besson, S., Duffiner, S., Barlaud, M., Aubert, G.: Segmentation of vectorial image features using shape gradients and information measures. JMIV 25(3), 365–386 (2006)CrossRefGoogle Scholar
  11. 11.
    Rousson, M., Brox, T., Deriche, R.: Active unsupervised texture segmentation on a diffusion based feature space. In: Proc. IEEE CVPR 2003, Madison, WI, USA, vol. 2, pp. 699–704 (2003)Google Scholar
  12. 12.
    Lee, S.M., Abott, A.L., Clark, N.A., Araman, P.A.: Active contours on statistical manifolds and texture segmentation. In: Proc. IEEE ICIP 2005, vol. 3, pp. 828–831 (2005)Google Scholar
  13. 13.
    Chan, T., Sandberg, B., Vese, L.: Active contours without edges for vector-valued images. JVCIR 11(2), 130–141 (2000)CrossRefGoogle Scholar
  14. 14.
    Martin, D., Fowlkes, C., Malik, J.: Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues. IEEE PAMI 26(5), 530–549 (2004)Google Scholar
  15. 15.
    Goldstein, T., Bresson, X., Osher, S.: Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction. In: Technical Report 06, Math. Department UCLA, Los Angeles, USA (2009)Google Scholar
  16. 16.
    Michailovich, O., Rathi, Y., Tannenbaum, A.: Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow. IEEE TIP 16(11), 2787–2801 (2007)MathSciNetGoogle Scholar
  17. 17.
    Raubera, T.W., Braunb, K.B.: Probabilistic distance measures of the Dirichlet and Beta distributions. Pattern Recognition 41(2), 637–645 (2008)CrossRefGoogle Scholar
  18. 18.
    Lecellier, F., Fadili, J., Jehan-Besson, S., Aubert, G., Revenu, M.: Region-based active contours and sparse representations for texture segmentation. In: Proc. IEEE ICPR 2008, Florida (2008)Google Scholar
  19. 19.
    Allili, M.S., Ziou, D., Bentabet, L.: A robust level set approach for image segmentation and statistical modeling. In: Proc. Adv. Conc. on Intelligent Vision Systems (ACIVS), pp. 243–251 (2004)Google Scholar
  20. 20.
    Sandberg, B., Chan, T., Vese, L.: A level-set and gabor-based active contour algorithm for segmenting textured images. In: Technical Report 39, Math. Department UCLA, Los Angeles, USA (2002)Google Scholar
  21. 21.
    Yin, W., Osher, S., Goldfarb, D., Darbon, J.: Bregman iterative algoruithms for l1 minimization with applications to compressed sensing. SIAM J. Imaging Sci. 1, 143–168 (2008)MATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    He, Y., Luo, Y., Hu, D.: Unsupervised Texture Segmentation via Applying Geodesic Active Regions to Gaborian Feature Space. IEEE Transactions on Engineering, Computing and Technology, 272–275 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Foued Derraz
    • 1
    • 2
  • Abdelmalik Taleb-Ahmed
    • 1
  • Antonio Pinti
    • 1
  • Laurent Peyrodie
    • 4
  • Nacim Betrouni
    • 3
  • Azzeddine Chikh
    • 2
  • Fethi Bereksi-Reguig
    • 2
  1. 1.LAMIH UMR CNRS 8530ValenciennesFrance
  2. 2.GBM LaboratoryAbou Bekr Belkaid universityTlemcenAlgeria
  3. 3.LAMIH UMR CNRS 8530Valenciennes
  4. 4.Hautes Etudes d’Ingénieur LilleFrance

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