Some Global Measures for Shape Retrieval

Part of the Studies in Computational Intelligence book series (SCI, volume 488)

Abstract

In this paper, we propose an efficient shape retrieval method. The idea is very simplistic, it is based on two global measures, the ellipse fitting and the minimum area rectangle. In this approach we don’t need any information about the shape structure or its boundary form, as in most shape matching methods, we have only to compute the relativity between the surface of the shape and both of the minimum area rectangle encompassing it and its ellipse fitting. The proposed method is invariant to similarity transformations (translation, isotropic scaling and rotation). In addition, the matching gives satisfying results with minimal cost. The retrieval performance is illustrated using the MPEG-7 shape database.

Keywords

ellipse fitting minimum area rectangle shape retrieval 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Liu, T.-L., Geiger, D.: Approximate tree matching and shape similarity. In: Proceedings of the IEEE International Conference on Computer Vision, Corfu, Greece, pp. 456–462 (1999)Google Scholar
  2. 2.
    Sharvit, D., Chan, J., Tek, H., Kimia, B.B.: Symmetry-based indexing of image databases, J. Visual Commun. Image Representation 9(4), 366–380 (1998)CrossRefGoogle Scholar
  3. 3.
    Siddiqi, K., Bouix, S., Tannenbaum, A., Zucker, S.W.: The Hamilton–Jacobi skeleton. In: Proceedings of the IEEE International Conference on Computer Vision, Corfu, Greece, pp. 828–834 (1999)Google Scholar
  4. 4.
    Pizer, S., Olivier, W., Bloomberg, S.: Hierarchical shape description via the multiresolution symmetric axis transform. IEEE Transactions on PAMI 9, 505–511 (1987)CrossRefGoogle Scholar
  5. 5.
    Rom, H., Medioni, G.: Hierarchical decomposition of axial shape description. IEEE Transactions on PAMI 15, 973–981 (1993)CrossRefGoogle Scholar
  6. 6.
    Gdalyahu, Y., Weinshall, D.: Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes. IEEE Trans. Pattern Anal. Mach. Intell. 21(12), 1312–1328 (1999)CrossRefGoogle Scholar
  7. 7.
    Petrakis, E.G.M., Diplaros, A., Milios, E.: Matching and retrieval of distorted and occluded shapes using dynamic programming. IEEE Trans. Pattern Anal. Mach. Intell. 24(11), 1501–1516 (2002)CrossRefGoogle Scholar
  8. 8.
    Mokhtarian, F.: Silhouette-based isolated object recognition through curvature scale space. IEEE Trans. Patt. Anal. Mach. Intell. 17, 539–544 (1995)CrossRefGoogle Scholar
  9. 9.
    Davies, R., Twining, C., Cootes, T., Waterton, J., Taylor, C.: A minimum description length approach to statistical shape modeling. IEEE Trans. Med. Imag. 21(5), 525–537 (2002)CrossRefGoogle Scholar
  10. 10.
    Hill, A., Taylor, C., Brett, A.: A framework for automatic landmark identification using a new method of nonrigid correspondence. IEEE Trans. Pattern Anal. Mach. Intell. 22(3), 241–251 (2000)CrossRefGoogle Scholar
  11. 11.
    Schneider, P.J., Eberly, D.H.: Geometric Tools for Computer Graphics. Textbook Binding (2002)Google Scholar
  12. 12.
    Coope, I.D.: Circle fitting by linear and nonlinear least squares. Journal of Optimization Theory and Applications 76(2), 381 (1993)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Kanatani, K., Rangarajan, P.: Hyperaccurate ellipse fitting without iterations. In: Proc. Int. Conf. Computer Vision Theory and Applications (VISAPP 2010), Angers, France, vol. 2, pp. 5–12 (May 2010)Google Scholar
  14. 14.
    Sedgewick, R.: Algorithms. Addison-Wesley- the classic in the field (1983)Google Scholar
  15. 15.
    Graham, R.L.: An efficient algorithm for determining the convex hull of a finite planar set. Information Processing Letters 1, 132–133 (1972)CrossRefMATHGoogle Scholar
  16. 16.
    Gander, W., Golub, G.H., Strebel, R.: Least-Squares Fitting of Circles and Ellipses. BIT 34, 558–578 (1994)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Latecki, L.J., Lakämper, R., Eckhardt, U.: Shape descriptors for non-rigid shapes with a single closed contour. In: Computer Vision and Patt. Recog.: CVPR 2000, pp. 1424–1429 (2000)Google Scholar
  18. 18.
    Kanatani, K.: Hyperaccurate Ellipse Fitting Without Iterations. In: Proc. Int. Conf. Computer Vision Theory and Applications (VISAPP 2010), Angers, France, vol. 2, pp. 5–12 (May 2010)Google Scholar
  19. 19.
    Stojmenovic, M., Nayak, A.: Direct Ellipse Fitting and Measuring Based on Shape Boundaries. In: Mery, D., Rueda, L. (eds.) PSIVT 2007. LNCS, vol. 4872, pp. 221–235. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Yu, J., Kulkarni, S.R., Vincent Poor, H.: Robust Fitting of Ellipses and Spheroids, arXiv:0912.1647v1 (December 2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of Sciences and Technology HOUARI BOUMEDIENEAlgiersAlgeria

Personalised recommendations