Dictionary Based Image Segmentation

  • Anders Bjorholm DahlEmail author
  • Vedrana Andersen Dahl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9127)


We propose a method for weakly supervised segmentation of natural images, which may contain both textured or non-textured regions. Our texture representation is based on a dictionary of image patches. To divide an image into separated regions with similar texture we use an implicit level sets representation of the curve, which makes our method topologically adaptive. In addition, we suggest a multi-label version of the method. Finally, we improve upon a similar texture representation, by formulating the computation of a texture probability in terms of a matrix multiplication. This results in an efficient implementation of our segmentation method. We experimentally validated our approach on a number of natural as well as composed images.


Local Binary Pattern Active Contour Probability Image Image Patch Active Contour Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. TPAMI 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  2. 2.
    Brox, T., Rousson, M., Deriche, R., Weickert, J.: Colour, texture, and motion in level set based segmentation and tracking. Image and Vision Computing 28(3), 376–390 (2010)CrossRefGoogle Scholar
  3. 3.
    Brox, T., Weickert, J.: A tv flow based local scale measure for texture discrimination. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3022, pp. 578–590. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  4. 4.
    Brox, T., Weickert, J.: A tv flow based local scale estimate and its application to texture discrimination. Journal of Visual Communication and Image Representation 17(5), 1053–1073 (2006)CrossRefGoogle Scholar
  5. 5.
    Caselles, V., Catté, F., Coll, T., Dibos, F.: A geometric model for active contours in image processing. Numerische mathematik 66(1), 1–31 (1993)zbMATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Chan, T., Vese, L.A.: An active contour model without edges. In: Nielsen, M., Johansen, P., Fogh Olsen, O., Weickert, J. (eds.) Scale-Space 1999. LNCS, vol. 1682, pp. 141–151. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  7. 7.
    Chan, T.F., Sandberg, B.Y., Vese, L.A.: Active contours without edges for vector-valued images. Journal of Visual Communication and Image Representation 11(2), 130–141 (2000)CrossRefGoogle Scholar
  8. 8.
    Chan, T.F., Vese, L.A.: Active contours without edges. TIP 10(2), 266–277 (2001)zbMATHGoogle Scholar
  9. 9.
    Dahl, A.B., Dahl, V.A.: Dictionary snakes. In: ICPR (2014)Google Scholar
  10. 10.
    Dahl, A.L., Larsen, R.: Learning dictionaries of discriminative image patches. In: BMVC (2011)Google Scholar
  11. 11.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  12. 12.
    Elad, M.: Sparse and redundant representations: from theory to applications in signal and image processing. Springer (2010)Google Scholar
  13. 13.
    Gao, Y., Bouix, S., Shenton, M., Tannenbaum, A.: Sparse texture active contour. TIP (2013)Google Scholar
  14. 14.
    Gibou, F., Fedkiw, R.: A fast hybrid k-means level set algorithm for segmentation. In: 4th Annual Hawaii International Conference on Statistics and Mathematics, pp. 281–291. Hawaii, USA (2005)Google Scholar
  15. 15.
    Goldenberg, R., Kimmel, R., Rivlin, E., Rudzsky, M.: Fast geodesic active contours. TIP 10(10), 1467–1475 (2001)MathSciNetGoogle Scholar
  16. 16.
    He, L., Osher, S.J.: Solving the chan-vese model by a multiphase level set algorithm based on the topological derivative. In: Sgallari, F., Murli, A., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 777–788. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  17. 17.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. IJCV 1(4), 321–331 (1988)CrossRefGoogle Scholar
  18. 18.
    Li, S.Z.: Markov random field modeling in computer vision. Springer-Verlag New York, Inc. (1995)Google Scholar
  19. 19.
    Mairal, J., Bach, F., Ponce, J.: Task-driven dictionary learning. TPAMI 34(4), 791–804 (2012)CrossRefGoogle Scholar
  20. 20.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Discriminative learned dictionaries for local image analysis. In: CVPR, pp. 1–8. IEEE (2008a)Google Scholar
  21. 21.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Supervised dictionary learning (2008b). arXiv preprint arXiv:0809.3083
  22. 22.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. IJCV 43(1), 7–27 (2001)zbMATHCrossRefGoogle Scholar
  23. 23.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: A level set approach. TPAMI 17(2), 158–175 (1995)CrossRefGoogle Scholar
  24. 24.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV, vol. 2, pp. 416–423. IEEE (2001)Google Scholar
  25. 25.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. TPAMI 24(7), 971–987 (2002)CrossRefGoogle Scholar
  26. 26.
    Peyré, G.: Sparse modeling of textures. Journal of Mathematical Imaging and Vision 34(1), 17–31 (2009)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Rother, C., Kolmogorov, V., Blake, A.: ”grabcut”: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)CrossRefGoogle Scholar
  28. 28.
    Rousson, M., Brox, T., Deriche, R.: Active unsupervised texture segmentation on a diffusion based feature space. In: CVPR, vol. 2, p. II-699. IEEE (2003)Google Scholar
  29. 29.
    Santner, J., Unger, M., Pock, T., Leistner, C., Saffari, A., Bischof, H.: Interactive texture segmentation using random forests and total variation. In: BMVC, pp. 1–12. Citeseer (2009)Google Scholar
  30. 30.
    Skretting, K., Husøy, J.H.: Texture classification using sparse frame-based representations. EURASIP journal on applied signal processing 2006, 102 (2006)Google Scholar
  31. 31.
    Song, B., Chan, T.: A fast algorithm for level set based optimization. UCLA Cam Report 2(68) (2002)Google Scholar
  32. 32.
    Varma, M., Garg, R.: Locally invariant fractal features for statistical texture classification. In: ICCV, pp. 1–8. IEEE (2007)Google Scholar
  33. 33.
    Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the mumford and shah model. IJCV 50(3), 271–293 (2002)zbMATHCrossRefGoogle Scholar
  34. 34.
    Yezzi Jr, A., Tsai, A., Willsky, A.: A statistical approach to snakes for bimodal and trimodal imagery. In: ICCV, vol. 2, pp. 898–903. IEEE (1999)Google Scholar
  35. 35.
    Zhao, H.K., Chan, T., Merriman, B., Osher, S.: A variational level set approach to multiphase motion. Journal of computational physics 127(1), 179–195 (1996)zbMATHMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkLyngbyDenmark

Personalised recommendations