The Visual Computer

, Volume 28, Issue 5, pp 475–491 | Cite as

Matching sequences of salient contour points characterized by Voronoi region features

  • Yuqing SongEmail author
  • Shuyuan Jin
Original Article


In this paper, we introduce a shape matching method by matching sequences of salient contour points that are characterized by Voronoi region features. The proposed approach is summarized as follows: (1) a sequence of salient contour points is selected using the Voronoi diagram of the contour point set, (2) the features of the salient points are computed based on the interior and exterior regions of the Voronoi diagram, and (3) a cyclic edit distance is used to match two shapes. Tests on the MPEG-7 and ETH-80 datasets demonstrated the effectiveness and efficiency of the proposed method.


Shape matching Voronoi tree Restricted merge-split edit distance Cyclic similarity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Veltkamp, R.C.: Shape matching: similarity measures and algorithms. Technical report UU-CS-2001-03, Utrecht University, 2001 Google Scholar
  2. 2.
    Pavlidis, T.: A review of algorithms for shape analysis. In: Comput. Graph. Image Process., vol. 7, pp. 243–258 (1978) Google Scholar
  3. 3.
    Sebastian, T.B., Kimia, B.B.: Curves vs skeletons in object recognition. In: ICIP (3), pp. 22–25 (2001) Google Scholar
  4. 4.
    Zahn, C., Roskie, R.: Fourier descriptors for plane closed curves. IEEE Trans. Comput. C-21(3), 269–281 (1972) CrossRefGoogle Scholar
  5. 5.
    Mokhtarian, F., Abbasi, S., Kittler, J.: Efficient and robust retrieval by shape content through curvature scale space. In: Image Databases and Multi-media Search, proceedings of the First International Workshop IDB-MMS’96, Amsterdam, The Netherlands, pp. 35–42 (1996) Google Scholar
  6. 6.
    Adoram, M., Lew, M.S.: IRUS: image retrieval using shape. In: IEEE International Conference on Multimedia Computing Systems, vol. 2, pp. 597–602 (1999) CrossRefGoogle Scholar
  7. 7.
    Arkin, E.M., Chew, L.P., Huttenlocher, D.P., Kedem, K., Mitchell, J.S.B.: An efficiently computable metric for comparing polygonal shapes. IEEE Trans. Pattern Anal. Mach. Intell. 13(3), 209–215 (1991) CrossRefGoogle Scholar
  8. 8.
    Rote, G.: Computing the Frechet distance between piecewise smooth curves. In: Proceeding of the 20th European Workshop on Computational Geometry, pp. 147–150 (2004) Google Scholar
  9. 9.
    Prokop, R.J., Reeves, A.P.: A survey of moment-based techniques for unoccluded object representation and recognition. Comput. Vis. Graph. Image Process. 54(5), 438–460 (1992) Google Scholar
  10. 10.
    Sclaroff, S.: Deformable prototypes for encoding shape categories in image databases. Pattern Recognit. 30(4), 627–641 (1997) CrossRefGoogle Scholar
  11. 11.
    Hagedoorn, M., Veltkamp, R.C.: Reliable and efficient pattern matching using an affine invariant metric. Int. J. Comput. Vis. 31, 203–225 (1999) CrossRefGoogle Scholar
  12. 12.
    Liu, T., Geiger, D.: Approximate tree matching and shape similarity. In: ICCV, pp. 456–462 (1999) Google Scholar
  13. 13.
    Siddiqi, K., Shokoufandeh, A., Dickinson, S.J., Zucker, S.W.: Shock graphs and shape matching. Int. J. Comput. Vis. 55(1), 13–32 (1999) CrossRefGoogle Scholar
  14. 14.
    Blum, H.: A transformation for extracting new descriptors of shape. In: Dunn, W. (ed.) Symposium Models for Speech and Visual Form, pp. 362–380. MIT Press, Cambridge (1967) Google Scholar
  15. 15.
    Arcelli, C., di Baja, G.S.: Ridge points in Euclidean distance maps. Pattern Recognit. Lett. 13(4), 237–243 (1992) CrossRefGoogle Scholar
  16. 16.
    Lee, Y.-H., Horng, S.-J.: The chessboard distance transform and the medial axis transform are interchangeable. In: The 10th International Parallel Processing Symposium, pp. 424–428 (1996) Google Scholar
  17. 17.
    Attali, D., Montanvert, A.: Computing and simplifying 2D and 3D continuous skeletons. Comput. Vis. Image Underst. 67(3), 261–273 (1997) CrossRefGoogle Scholar
  18. 18.
    Zhu, S.C., Yuille, A.L.: FORMS: A flexible object recognition and modeling system. Int. J. Comput. Vis. 20(3), 187–212 (1996) CrossRefGoogle Scholar
  19. 19.
    Hiransakolwong, N., Vu, K., Hua, K.A., Lang, S.-D.: Shape recognition based on the medial axis approach. In: ICME, pp. 257–260 (2004) Google Scholar
  20. 20.
    Klein, P., Tirthapura, S., Sharvit, D., Kimia, B.: A tree-edit-distance algorithm for comparing simple, closed shapes. In: Proceedings of the 11th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 696–704 (2000) Google Scholar
  21. 21.
    Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of shapes by editing their shock graphs. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 550–571 (2004) CrossRefGoogle Scholar
  22. 22.
    Gusfield, D.: Algorithms on Strings, Trees and Sequences—Computer Science and Computational Biology. Cambridge University Press, Cambridge (1997) zbMATHCrossRefGoogle Scholar
  23. 23.
    Maes, M.: On a cyclic string-to-string correction problem. Inf. Process. Lett. 35(2), 73–78 (1990) MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Bille, P.: A survey on tree edit distance and related problems. Theor. Comput. Sci. 337(1–3), 217–239 (2005) MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Klein, P.N.: Computing the edit-distance between unrooted ordered trees. In: Proc. 6th Ann. European Symp. on Algorithms (ESA), pp. 91–102. Springer, Berlin (1998) Google Scholar
  26. 26.
    Bille, P.: Ordered tree edit distance with merge and split operations. Technical report TR-2003-35. IT University of Copenhagen, September, 2003 Google Scholar
  27. 27.
    Shamos, M.I., Hoey, D.: Closest-point problems. In: Proc. 16th Annu. IEEE Sympos. Found. Comput. Sci, pp. 151–162 (1975) Google Scholar
  28. 28.
    Marzal, A., Palazón, V.: Dynamic time warping of cyclic strings for shape matching. In: Pattern Recognition and Image Analysis. Lecture Notes in Computer Science, vol. 3687, pp. 644–652 (2005) CrossRefGoogle Scholar
  29. 29.
    Latecki, L., Lakämper, R., Eckhardt, U.: Shape descriptors for non-rigid shapes with a single closed contour. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2000) Google Scholar
  30. 30.
    Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: CVPR (2003) Google Scholar
  31. 31.
    Super, B.J.: Improving object recognition accuracy and speed through non-uniform sampling. In: Proc. SPIE Conference Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision, Providence, RI. SPIE, vol. 5267, pp. 228–239 (2003) Google Scholar
  32. 32.
    Super, B.: Learning chance probability functions for shape retrieval or classification. In: IEEE Workshop on Learning in Comp. Vis. and Pat. Recog. (2004) Google Scholar
  33. 33.
    Attalla, E., Siy, P.: Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching. Pattern Recognit. 38, 2229–2241 (2005) CrossRefGoogle Scholar
  34. 34.
    McNeill, G.: Probabilistic shape matching and part decomposition. In: International Conference on Pattern Recognition (2006) Google Scholar
  35. 35.
    Felzenszwalb, P.F., Schwartz, J.: Hierarchical matching of deformable shapes. In: CVPR (2007) Google Scholar
  36. 36.
    Bai, X., Yang, X., Latecki, L.J., Liu, W., Tu, Z.: Learning context sensitive shape similarity by graph transduction. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 861–874 (2010) CrossRefGoogle Scholar
  37. 37.
    Yang, X., Koknar-Tezel, S., Latecki, L.J.: Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval. In: CVPR (2009) Google Scholar
  38. 38.
    Ding, L., Belkin, M.: Probabilistic mixtures of differential profiles for shape recognition. In: Proceedings of International Conference on Pattern Recognition (ICPR) (2008) Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Tianjin University of Technology and EducationTianjinChina
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

Personalised recommendations