Image Segmentation Using Normalized Cuts and Efficient Graph-Based Segmentation

  • Narjes Doggaz
  • Imene Ferjani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6979)

Abstract

In this paper we propose an hybrid segmentation algorithm which incorporates the advantages of the efficient graph based segmentation and normalized cuts partitioning algorithm. The proposed method requires low computational complexity and is therefore suitable for real-time image segmentation processing. Moreover, it provides effective and robust segmentation. For that, our method consists first, at segmenting the input image by the “Efficient Graph-Based” segmentation. The segmented regions are then represented by a graph structure. As a final step, the normalized cuts partitioning algorithm is applied to the resulting graph in order to remove non-significant regions. In the proposed method, the main computational cost is the efficient graph based segmentation cost since the computational cost of partitioning regions using the Ncut method is negligibly small. The efficiency of the proposed method is demonstrated through a large number of experiments using different natural scene images.

Keywords

Image Segmentation Normalized Cuts Efficient graph-based Region adjacency graph 

References

  1. 1.
    Graph based image segmentation tutorial (November 21, 2007), http://www.cis.upenn.edu/~jshi/graphtutorial/
  2. 2.
    Adamek, T., Connor, E., Murphy, N.: Region-based segmentation of images using syntactic visual features. In: 6th International Workshop on Image Analysis for Multimedia Interactive Services (April 2005)Google Scholar
  3. 3.
    Berkeley: Berkeley segmentation and boundary detection benchmark and dataset (2003), http://www.cs.berkeley.edu/projects/vision/grouping/segbench
  4. 4.
    Chen, T.W., Chen, Y.L., Chien, S.Y.: Fast image segmentation based on k-means clustering with histograms in hsv color space. In: Multimedia Signal Processing, pp. 322–325 (2008)Google Scholar
  5. 5.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)CrossRefGoogle Scholar
  6. 6.
    Cour, T., Benezit, F., Shi, J.: Spectral segmentation with multiscale graph decomposition. In: Computer Vision and Pattern Recognition, CVPR (2005)Google Scholar
  7. 7.
    Cour, T., Yu, S., Shi, J.: Normalized cuts matlab code, http://www.cis.upenn.edu/~jshi/software
  8. 8.
    Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. Pattern Analysis and Machine Intelligence 35, 800–810 (2001)CrossRefGoogle Scholar
  9. 9.
    Donoser, M., Bischof, H.: Roi-seg: Unsupervised color segmentation by combining differently focused sub results. In: Computer Vision and Pattern Recognition (CVPR) (2007)Google Scholar
  10. 10.
    Felzenszwalb, P., Huttenlocher, P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59, 167–181 (2004)CrossRefGoogle Scholar
  11. 11.
    Freixenet, J., Muñoz, X., Raba, D., Martí, J., Cufí, X.: Yet another survey on image segmentation: Region and boundary information integration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 408–422. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Hoon, T., Mu, K., Uk Lee, S.: Learning full pairwise affinities for spectral segmentation. In: Computer Vision and Pattern Recognition (CVPR), pp. 2101–2108 (2010)Google Scholar
  13. 13.
    Makrogiannis, S., Economou, G., Fotopoulos, S.: A region dissimilarity relation that combines feature-space and spatial information for color image segmentation. IEEE Transactions Systems, Man, Cybernetics Part B 35, 44–53 (2005)CrossRefGoogle Scholar
  14. 14.
    Martin, D., Fowlkes, C., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Technical report, EECS Department. University of California, Berkeley, Janvier (2001)Google Scholar
  15. 15.
    Maxwell, A., Shafer, A.: Physics-based segmentation of complex objects using multiple hypotheses of image formation. Computer Vision And Image Understanding 65, 269–295 (1997)CrossRefGoogle Scholar
  16. 16.
    Meila, M.: Comparing clusterings by the variation of information. Journal of Multivariate Analysis, 173–187 (2003)Google Scholar
  17. 17.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transaction on Pattern Analysis and Maching Intelligence 22, 888–905 (2000)CrossRefGoogle Scholar
  18. 18.
    Tao, W., Jin, H., Zhang, Y.: Color image segmentation based on mean shift and normalized cuts. IEEE Transactions on, Systems and Cybernetics-Part B 37, 1382–1388 (2007)CrossRefGoogle Scholar
  19. 19.
    Tatiraju, S., Mehta, A.: Image segmentation using k-means clustering, em and normalized cuts. Technical reportGoogle Scholar
  20. 20.
    Thiran, J., Warscotte, V., Macq, B.: A queue-based region growing algorithm for accurate segmentation of multi-dimensional digital images. Signal Processing 60, 1–10 (1997)CrossRefMATHGoogle Scholar
  21. 21.
    Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. Pattern Analysis and Machine Intelligence 29, 929–944 (2007)CrossRefGoogle Scholar
  22. 22.
    Wang, J., Jia, Y.: Normalized tree partitioning for image segmentation. In: Computer Vision and Pattern Recognition, CVPR (2008)Google Scholar
  23. 23.
    Weiss, Y.: Segmentation using eigenvectors: A unifying view. In: Seventh International Conference on Computer Vision (ICCV 1999), vol. 2, p. 975 (1999)Google Scholar
  24. 24.
    Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1101–1113 (1993)CrossRefGoogle Scholar
  25. 25.
    Yu, S., Shi, J.: Multiclass spectral clustering. In: International Conference on Computer Vision (ICCV), pp. 313–319 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Narjes Doggaz
    • 1
  • Imene Ferjani
    • 1
  1. 1.Computer Science Department, Faculty of SciencesURPAHTunisTunisia

Personalised recommendations