Multiphase Tensor Level-Set Method for Segmentation of Natural Images

  • Vladimir Lekić
  • Zdenka Babić
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 102)


Methods for processing digital images based on partial differential equations (PDE) have been intensively developed in image analysis since 1990s. Recently presented works which follow this approach show good results in image segmentation. In this work we present a novel method based on a tensor level-set approach. By using superpixel oversegmentation as one of the image cues we simplify evolution of level-set equation. This approach allows us to extend tensor level-set to multiphase tensor-level set method. To make our model applicable on color images we use cues obtained from CIELAB color space. Evaluation results show that our method achieves better segmentation results than the existing models based on level-set framework.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Comm. on Pure and Applied Math. 42(5), 577–685 (1989)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Chan, T.F., Vese, L.A.: Active Contours Without Edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Osher, S.J., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. Springer, Heidelberg (2002)Google Scholar
  4. 4.
    Yezrielev Sandberg, B., Chan, T.F., Vese, L.A.: Active contours without edges for vector-valued images. Journal of Visual Communication and Image Representation 11, 130–141 (2000)CrossRefGoogle Scholar
  5. 5.
    Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the mumford and shah model. Int. Journal of Comp. Vision 50, 271–293 (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Tao, D., Wang, B., Gao, X., Li, X.: A unified tensor level set for image segmentation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40, 857–867 (2010)CrossRefGoogle Scholar
  7. 7.
    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. Citeseer (2005)Google Scholar
  8. 8.
    Bertelli, L., Sumengen, B., Manjunath, B.S., Gibou, F.: A variational framework for multiregion pairwise-similarity-based image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1400–1414 (2007)Google Scholar
  9. 9.
    Martin, D.R., Fowlkes, C.C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms. In: Workshop on Perceptual Organization in Computer Vision (2001)Google Scholar
  10. 10.
    Ren, X.F., Malik, J.: Learning a classification model for segmentation. In: ICCV, pp. 10–17 (2003)Google Scholar
  11. 11.
    Fowlkes, C., Arbeláez, P., Maire, M., Malik, J.: Contour Detection and Hierarchical Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vladimir Lekić
    • 1
  • Zdenka Babić
    • 1
  1. 1.Facultiy of Electrical EngineeringUniversity of Banja LukaRepublika SrpskaBosnia and Herzegovina

Personalised recommendations