Combination of Multiple Segmentations by a Random Walker Approach

  • Pakaket Wattuya
  • Xiaoyi Jiang
  • Kai Rothaus
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5096)


In this paper we propose an algorithm for combining multiple image segmentations to achieve a final improved segmentation. In contrast to previous works we consider the most general class of segmentation combination, i.e. each input segmentation can have an arbitrary number of regions. Our approach is based on a random walker segmentation algorithm which is able to provide high-quality segmentation starting from manually specified seeds. We automatically generate such seeds from an input segmentation ensemble. Two applications scenarios are considered in this work: Exploring the parameter space and segmenter combination. Extensive tests on 300 images with manual segmentation ground truth have been conducted and our results clearly show the effectiveness of our approach in both situations.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cingue, L., Cucciara, R., Levialdi, S., Martinez, S., Pignalberi, G.: Optimal range segmentation parameters through genetic algorithms. In: Proc. of 15th Int. Conf. on Pattern Recognition, vol. 1, pp. 474–477 (2000)Google Scholar
  2. 2.
    Cour, T., Benezit, F., Shi, J.: Spectral segmentation with multiscale graph decomposition. In: Proc. of CVPR, pp. 1124–1131 (2005)Google Scholar
  3. 3.
    Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Int. Journal of Computer Vision, 167–181 (2004)Google Scholar
  4. 4.
    Grady, L.: Random walks for image segmentation. IEEE Trans. Patt. Anal. Mach. Intell 28, 1768–1783 (2006)CrossRefGoogle Scholar
  5. 5.
    Jiang, T., Zhou, Z.-H.: SOM ensemble-based image segmentation. Neural Processing Letters 20, 171–178 (2004)CrossRefGoogle Scholar
  6. 6.
    Keuchel, J., Küttel, D.: Efficient combination of probabilistic sampling approximations for robust image segmentation. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 41–50. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    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. 8th Int. Conf. Computer Vision, vol. 2, pp. 416–423 (2001)Google Scholar
  8. 8.
    Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Patt. Anal. Mach. Intell. 26(5), 530–539 (2004)CrossRefGoogle Scholar
  9. 9.
    Min, J., Powell, M., Bowyer, K.W.: Automated performance evaluation of range image segmentation algorithms. IEEE Trans. on SMC – Part B 34(1), 263–271 (2004)CrossRefGoogle Scholar
  10. 10.
    Rohlfing, T., Russakoff, D.B., Maurer Jr., C.R.: Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation. IEEE Trans. Medical Imaging 23, 983–994 (2004)CrossRefGoogle Scholar
  11. 11.
    Rohlfing, T., Maurer Jr., C.R.: Shape-based averaging for combination of multiple segmentations. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 838–845. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Pakaket Wattuya
    • 1
  • Xiaoyi Jiang
    • 1
  • Kai Rothaus
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of MünsterGermany

Personalised recommendations