Contour-Based Shape Retrieval Using Dynamic Time Warping

  • Andrés Marzal
  • Vicente Palazón
  • Guillermo Peris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)


A dissimilarity measure for shapes described by their contour, the Cyclic Dynamic Time Warping (CDTW) dissimilarity, is introduced. The dissimilarity measure is based on Dynamic Time Warping of cyclic strings, i.e., strings with no definite starting/ending points. The Cyclic Edit Distance algorithm by Maes cannot be directly extended to compute the CDTW dissimilarity, as we show in the paper. We present an algorithm that computes the CDTW dissimilarity in O(mnlogn) time, where m and n are the lengths of the cyclic strings. Shape retrieval with the new dissimilarity measure is experimentally compared with the WARP system on a standard corpus.


Optimal Path Dynamic Time Warping Dissimilarity Measure Optimal Alignment Edit Operation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bartolini, I., Ciaccia, P., Patella, M.: WARP: Accurate Retrieval of Shapes Using Phase of Fourier Descriptors and Time Warping Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(1), 142–147 (2005)CrossRefGoogle Scholar
  2. 2.
    Bunke, H., Bühler, H.: Applications of Approximate String Matching to 2D Shape Recognition. Pattern Recognition 26(12), 1797–1812 (1993)CrossRefGoogle Scholar
  3. 3.
    Folkers, A., Samet, H.: Content-based Image Retrieval Using Fourier Descriptors on a Logo Database. In: Proc of the 16th Int Conf on Pattern Recognition, pp. 521–524 (2002)Google Scholar
  4. 4.
    Maes, M.: On a Cyclic String-to-String Correction Problem. Information Processing Letters 35, 73–78 (1990)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Maes, M.: Polygonal Shape Recognition using String Matching Techniques. Pattern Recognition 24(5), 433–440 (1991)CrossRefGoogle Scholar
  6. 6.
    Marzal, A., Barrachina, S.: Speeding up the computation of the edit distance for cyclic strings. In: Int. Conference on Pattern Recognition, pp. 271–280 (2000)Google Scholar
  7. 7.
    Mollineda, R.A., Vidal, E., Casacuberta, F.: Efficient Techniques for a very Accurate Measurement of Dissimilarities between Cyclic Patterns, vol. 1876, pp. 121–126. Springer, Heidelberg (2000)Google Scholar
  8. 8.
    Peris, G., Marzal, A.: Fast Cyclic Edit Distance Computation with Weighted Edit Costs in Classification. In: Proc. Int. Conf. on Pattern Recognition, vol. 4 (2002)Google Scholar
  9. 9.
    Sankoff, D., Kruskal, J. (eds.): Time warps, string edits, and macromolecules: the theory and practice of sequence comparison. Addison-Wesley, Reading (1983)Google Scholar
  10. 10.
    Sebastian, T.B., Klein, P.N., Kimia, B.B.: On aligning curves. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(1), 116–125 (2003)CrossRefGoogle Scholar
  11. 11.
    Wagner, R.A., Fischer, M.J.: The String-to-String Correction Problem. Journal of ACM 21(1), 168–173 (1974)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Zhang, D., Lu, G.: A Comparative Study of Fourier Descriptors for Shape Representation and Retrieval. In: 5th Asian Conf. on Computer Vision (January 2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andrés Marzal
    • 1
  • Vicente Palazón
    • 1
  • Guillermo Peris
    • 1
  1. 1.Dept. Llenguatges i Sistemes InformàticsUniversitat Jaume I de CastellóSpain

Personalised recommendations