Automatic Color-Texture Image Segmentation by Using Active Contours

  • Mohand Saïd Allili
  • Djemel Ziou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4153)


In this paper, we propose a novel method for unsupervised color-texture segmentation. The approach aims at combining color and texture features and active contours to build a fully automatic segmentation algorithm. By fully automatic, we mean the steps of region initialization and calculation of the number of regions are performed automatically by the algorithm. Furthermore, the approach combines boundary and region information for accurate region boundary localization. We validate the approach by examples of synthetic and natural color-texture image segmentation.


Color texture boundary active contours automatic segmentation 


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  1. 1.
    Akaike, H.: A New Look at the Statistical Model Identification. IEEE Trans. on Automatic Control 19, 716–723 (1974)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Allili, M.S., Ziou, D.: An Automatic Segmentation Combining Mixture Analysis and Adaptive Region Information: A Level Set Approach. In: Proceedings of IEEE CRV, pp. 73–80 (2005)Google Scholar
  3. 3.
    Allili, M.S., Bouguila, N., Ziou, D.: Generalized Gaussian Mixture and MML, Technical ReportGoogle Scholar
  4. 4.
    Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying. IEEE Trans. on PAMI 24(8), 1026–1038 (2002)Google Scholar
  5. 5.
    Caselles, V., Kimmel, R., Shapiro, G.: Geodesic Active Contours. In: IJCV, vol. 22, pp. 61–79 (1997)Google Scholar
  6. 6.
    Freixenet, J., Munoz, X., Marti, J., Llado, X.: Colour Texture Segmentation by Region-Boundary Cooperation. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 250–261. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Huang, J., Kumar, S.R., Mitra, M., Zhu, W.-J., Zabih, R.: Image Indexing Using Color Correlograms. In: Proceedings of IEEE CVPR, pp. 762–768 (1997)Google Scholar
  8. 8.
    Jain, A., Farrokhnia, F.: Unsupervised Texture Segmentation by Using Gabor Filters. Pattern Recognition 24, 1167–1186 (1991)CrossRefGoogle Scholar
  9. 9.
    Liapis, S., Sifakis, E., Tziritas, G.: Colour and Texture Segmentation Using Wavelet Frame Analysis, Deterministic Relaxation and Fast Marching Algorithms. JVCIR 15(1), 1–26 (2004)CrossRefGoogle Scholar
  10. 10.
    Osher, S., Sethian, J.: Fronts Propagating with Curvature-dependant Speed: Algorithms Based on Hammilton-Jacobi Formulations. Journal of Computational Physics 22, 12–49 (1988)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Paragios, N., Deriche, R.: Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation. In: IJCV, pp. 223–247 (2002)Google Scholar
  12. 12.
    Rousson, M., Brox, T., Deriche, R.: Active Unsupervised Texture Segmentation on a Diffusion Based Feature Space. In: Proceedings of IEEE CVPR, vol. 2, pp. 699–704 (2003)Google Scholar
  13. 13.
    Sifakis, E., Garcia, C., Tziritas, G.: Bayesian Level Sets for Image Segmentation. JVCIR 13, 44–64 (2002)CrossRefGoogle Scholar
  14. 14.
    Yezzi, A., Tsai, A., Willsky, A.: A Fully Global Approach to Image Segmentation Via Couples Curve Evolution Equations. JVCIR 13, 195–216 (2002)CrossRefGoogle Scholar
  15. 15.
    Zhu, S., Yuille, A.: Region competition: Unifying Snakes, Region Growing and Bayes/MDL for Multiband Image Segmentation. IEEE Trans. PAMI 18, 884–900 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mohand Saïd Allili
    • 1
  • Djemel Ziou
    • 1
  1. 1.Faculty of Science, Department of Computer ScienceSherbrooke UniversitySherbrookeCanada

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