Segmentation Using SubMarkov Random Walk

  • Xingping Dong
  • Jianbing Shen
  • Luc Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8932)


In this paper, we propose a subMarkov random walk (subRW) with the label prior with added auxiliary nodes for seeded image segmentation. We unify the proposed subRW and the other popular random walk algorithms. This unifying view can transfer the intrinsic findings between different random walk algorithms, and offer the new ideas for designing the novel random walk algorithms by changing the auxiliary nodes. According to the second benefit, we design a subRW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts. The experimental results on natural images with twigs demonstrate that our algorithm achieves better performance than the previous random walk algorithms.


Segmentation subMarkov random walk auxiliary nodes 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Calinon, S., Guenter, F., Billard, A.: On learning, representing and generalizing a task in a humanoid robot. IEEE Trans. on Systems, Man and Cybernetics, Part B 37(2), 286–298 (2007)CrossRefGoogle Scholar
  2. 2.
    Couprie, C., Grady, L., Najman, L., Talbot, H.: Power watershed: A unifying graph-based optimization framework. IEEE Trans. on Pattern Analysis and Machine Intelligence 33(7), 1384–1399 (2011)CrossRefGoogle Scholar
  3. 3.
    Grady, L.: Multilabel random walker image segmentation using prior models. In: IEEE CVPR, pp. 763–770 (2005)Google Scholar
  4. 4.
    Grady, L.: Random walks for image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  5. 5.
    Grady, L., Funka-Lea, G.: Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials. In: Sonka, M., Kakadiaris, I.A., Kybic, J. (eds.) CVAMIA/MMBIA 2004. LNCS, vol. 3117, pp. 230–245. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Jegelka, S., Bilmes, J.: Submodularity beyond submodular energies: coupling edges in graph cuts. In: IEEE CVPR, pp. 1897–1904 (2011)Google Scholar
  7. 7.
    Kim, T.H., Lee, K.M., Lee, S.U.: Generative image segmentation using random walks with restart. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 264–275. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Kohli, P., Osokin, A., Jegelka, S.: A principled deep random field model for image segmentation. In: IEEE CVPR, pp. 1971–1978 (2013)Google Scholar
  9. 9.
    Lawler, G.F., Limic, V.: Random walk: a modern introduction. Cambridge University Press (2010)Google Scholar
  10. 10.
    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: IEEE ICCV, vol. 2, pp. 416–423 (2001)Google Scholar
  11. 11.
    Peng, J., Shen, J., Jia, Y., Li, X.: Saliency cut in stereo images. In: IEEE ICCVW, pp. 22–28 (2013)Google Scholar
  12. 12.
    Qiu, H., Hancock, E.R.: Clustering and embedding using commute times. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(11), 1873–1890 (2007)CrossRefGoogle Scholar
  13. 13.
    Shen, J., Du, Y., Wang, W., Li, X.: Lazy random walks for superpixel segmentation. IEEE Trans. on Image Processing 23(4), 1451–1462 (2014)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Sinop, A.K., Grady, L.: A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: IEEE ICCV, pp. 1–8 (2007)Google Scholar
  15. 15.
    Vicente, S., Kolmogorov, V., Rother, C.: Graph cut based image segmentation with connectivity priors. In: IEEE CVPR, pp. 1–8 (2008)Google Scholar
  16. 16.
    Wu, X.M., Li, Z., So, A.M., Wright, J., Chang, S.F.: Learning with partially absorbing random walks. In: NIPS, pp. 3077–3085 (2012)Google Scholar
  17. 17.
    Zhu, X., Nejdl, W., Georgescu, M.: An adaptive teleportation random walk model for learning social tag relevance. In: ACM SIGIR, pp. 223–232 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xingping Dong
    • 1
  • Jianbing Shen
    • 1
  • Luc Van Gool
    • 2
  1. 1.Beijing Laboratory of Intelligent Information Technology, School of Computer ScienceBeijing Institute of TechnologyBeijingP.R. China
  2. 2.Computer Vision LaboratoryETH ZurichZurichSwitzerland

Personalised recommendations