Generative Image Segmentation Using Random Walks with Restart

  • Tae Hoon Kim
  • Kyoung Mu Lee
  • Sang Uk Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5304)


We consider the problem of multi-label, supervised image segmentation when an initial labeling of some pixels is given. In this paper, we propose a new generative image segmentation algorithm for reliable multi-label segmentations in natural images. In contrast to most existing algorithms which focus on the inter-label discrimination, we address the problem of finding the generative model for each label. The primary advantage of our algorithm is that it produces very good segmentation results under two difficult problems: the weak boundary problem and the texture problem. Moreover, single-label image segmentation is possible. These are achieved by designing the generative model with the Random Walks with Restart (RWR). Experimental results with synthetic and natural images demonstrate the relevance and accuracy of our algorithm.


  1. 1.
    Mortensen, E.N., Barrett, W.A.: Interactive segmentation with intelligent scissors. Graphical Models in Image Process. 60(5), 349–384 (1998)CrossRefzbMATHGoogle Scholar
  2. 2.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. IJCV V1(4), 321–331 (1988)CrossRefzbMATHGoogle Scholar
  3. 3.
    Sethian, J.A.: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press, Cambridge (1999)zbMATHGoogle Scholar
  4. 4.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. IJCV 70(2), 109–131 (2006)CrossRefGoogle Scholar
  5. 5.
    Bai, X., Sapiro, G.: A geodesic framework for fast interactive image and video segmentation and matting. In: Proc. ICCV 2007, pp. 1–8 (2007)Google Scholar
  6. 6.
    Grady, L.: Random walks for image segmentation. PAMI 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  7. 7.
    Sinop, A.K., Grady, L.: A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: Proc. ICCV (2007)Google Scholar
  8. 8.
    Pan, J.Y., Yang, H.J., Faloutsos, C., Duygulu, P.: Automatic multimedia cross-modal correlation discovery. In: KDD 2004, pp. 653–658 (2004)Google Scholar
  9. 9.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)zbMATHGoogle Scholar
  10. 10.
    Grady, L., Schwartz, E.L.: Isoperimetric graph partitioning for image segmentation. PAMI 28(3), 469–475 (2006)CrossRefGoogle Scholar
  11. 11.
    Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Scholkopf, B.: Learning with local and global consistency. In: NIPS (2003)Google Scholar
  12. 12.
    He, J., Li, M., Zhang, H.J., Tong, H., Zhang, C.: Manifold-ranking based image retrieval. In: ACM Multimedia, pp. 9–16 (2004)Google Scholar
  13. 13.
    Tong, H., Faloutsos, C.: Center-piece subgraphs: problem definition and fast solutions. In: KDD 2006, pp. 404–413 (2006)Google Scholar
  14. 14.
    Sun, J., Qu, H., Chakrabarti, D., Faloutsos, C.: Neighborhood formation and anomaly detection in bipartite graphs. In: ICDM 2005, pp. 418–425 (2005)Google Scholar
  15. 15.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22(8), 888–905 (2000)CrossRefGoogle Scholar
  16. 16.
    Tong, H., Faloutsos, C., Pan, J.Y.: Fast random walk with restart and its applications. In: ICDM 2006, pp. 613–622 (2006)Google Scholar
  17. 17.
    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: Proc. ICCV, vol. 2, pp. 416–423 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Tae Hoon Kim
    • 1
  • Kyoung Mu Lee
    • 1
  • Sang Uk Lee
    • 1
  1. 1.Dept. of EECS, ASRISeoul National UniversitySeoulKorea

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