Contour Grouping with Partial Shape Similarity

  • Chengqian Wu
  • Xiang Bai
  • Quannan Li
  • Xingwei Yang
  • Wenyu Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


In this paper, a novel algorithm is introduced to group contours from clutter images by integrating high-level information (prior of part segments) and low-level information (paths of segmentations of clutter images). The partial shape similarity between these two levels of information is embedded into the particle filter framework, an effective recursively estimating model. The particles in the framework are modeled as the paths on the edges of segmentation results (Normalized Cuts in this paper). At prediction step, the paths extend along the edges of Normalized Cuts; while, at the update step, the weights of particles update according to their partial shape similarity with priors of the trained contour segments. Successful results are achieved against the noise of the testing image, the inaccuracy of the segmentation result as well as the inexactness of the similarity between the contour segment and edges segmentation. The experimental results also demonstrate robust contour grouping performance in the presence of occlusion and large texture variation within the segmented objects.


Contour grouping partial shape similarity particle filter Normalized Cuts 


  1. 1.
    Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Contexts. PAMI (2002)Google Scholar
  2. 2.
    Sun, K., Super, B.J.: Classification of Contour Shapes Using Class Segment Sets. In: CVPR (2005)Google Scholar
  3. 3.
    Ling, H., Jacobs, D.W.: Shape Classification Using the Inner-Distance. PAMI 29(2), 286–299 (2007)CrossRefGoogle Scholar
  4. 4.
    Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. In: CVPR (1997)Google Scholar
  5. 5.
    Borenstein, E., Malik, J.: Shape Guided Object Segmentation. In: CVPR (2006)Google Scholar
  6. 6.
    Srinivasan, P., Shi, J.: Bottom-up Recognition and Parsing of the Human Body. In: CVPR (2007)Google Scholar
  7. 7.
    Ren, X., Fowlkes, C., Malik, J.: Cue Integration in Figure/ground Labeling. In: NIPS (2005)Google Scholar
  8. 8.
    Zheng, S., Tu, Z., Yuille, A.: Detecting Object Boundaries Using Low-, Mid-, and High-Level Information. In: CVPR (2007)Google Scholar
  9. 9.
    Kumar, M.P., Torr, P.H.S., Zisserman, A.: OBJ CUT. In: CVPR (2005)Google Scholar
  10. 10.
    Black, M.J., Fleet, D.J.: Probabilistic detection and tracking of motion boundaries. IJCV 38(3), 231–245 (2000)CrossRefzbMATHGoogle Scholar
  11. 11.
    Pérez, P., Blake, A., Gangnet, M.: Jetstream: Probabilistic contour extraction with particles. In: ICCV, pp. 524–531 (2001)Google Scholar
  12. 12.
    Adluru, N., Latecki, L.J., Lakaemper, R., Young, T., Bai, X., Gross, A.: Contour Grouping Based on Local Symmetry. In: ICCV (2007)Google Scholar
  13. 13.
    Borenstein, E., Sharon, E., Ullman, S.: Combining top-down and bottom-up segmentation. In: Proc. IEEE workshop on Perc. Org. in Com. Vis. (2004)Google Scholar
  14. 14.
    McNeill, G., Vijayakumar, S.: Part-based Probabilistic Point Matching Using Equivalence Constraints. In: NIPS (2006)Google Scholar
  15. 15.
    Zöllor, T., Buhumann, J.M.: Robust Image Segmentation Using Resampling and Shape Constraints. PAMI 29(7), 1147–1164 (2007)CrossRefGoogle Scholar
  16. 16.
    Levin, A., Weiss, Y.: Learning to combine bottom-up and top-down segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 581–594. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Tu, Z., Chen, X., Yuille, A., Zhu, S.C.: Image parsing: unifying segmentation, detection, and object recognition. IJCV (2005)Google Scholar
  18. 18.
    Shotton, J., Blake, A., Cipolla, R.: Contour-Based Learning for Object Detection. In: ICCV (2005)Google Scholar
  19. 19.
    Cremers, D., Kohlberger, T., Schnörr, C.: Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition 36, 1929–1943 (2003)CrossRefzbMATHGoogle Scholar
  20. 20.
    Tu, Z., Yuille, A.: Shape Matching and Recognition: Using Generative Models and Informative Features. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 195–209. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chengqian Wu
    • 1
  • Xiang Bai
    • 1
  • Quannan Li
    • 1
  • Xingwei Yang
    • 2
  • Wenyu Liu
    • 1
  1. 1.Dept. of Electronics and Information EngineeringHuazhong University of Science and TechnologyWuhanP.R. China
  2. 2.Dept. of Computer and Information SciencesTemple UniversityPhiladelphiaUSA

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