Edit Distance for Ordered Vector Sets: A Case of Study

  • Juan Ramón Rico-Juan
  • José M. Iñesta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)

Abstract

Digital contours in a binary image can be described as an ordered vector set. In this paper an extension of the string edit distance is defined for its computation between a pair of ordered sets of vectors. This way, the differences between shapes can be computed in terms of editing costs. In order to achieve efficency a dominant point detection algorithm should be applied, removing redundant data before coding shapes into vectors. This edit distance can be used in nearest neighbour classification tasks. The advantages of this method applied to isolated handwritten character classification are shown, compared to similar methods based on string or tree representations of the binary image.

Topics

Dominant Points Pattern Recognition Structural Pattern Recognition 
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References

  1. 1.
    Rico-Juan, J.R., Micó, L.: Comparison of AESA and LAESA search algorithms using string and tree edit distances. Pattern Recognition Letters 24(9), 1427–1436 (2003)CrossRefGoogle Scholar
  2. 2.
    Freeman, H.: On the encoding of arbitrary geometric configurations. IRE Transactions on Electronic Computer 10, 260–268 (1961)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Rico-Juan, J.R., Micó, L.: Some results about the use of tree/string edit distances in a nearest neighbour classification task. In: Perales, F.J., Campilho, A.C., Pérez, N., Sanfeliu, A. (eds.) IbPRIA 2003. LNCS, vol. 2652, pp. 821–828. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. J. ACM 21, 168–173 (1974)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Zhang, K., Shasha, D.: Simple fast algorithms for the editing distance between trees and related problems. SIAM Journal of Computing 18, 1245–1262 (1989)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Teh, C.H., Chin, R.T.: On the detection of dominant points on digital curves. IEEE Trans. Pattern Anal. Mach. Intell. 11, 859–872 (1989)CrossRefGoogle Scholar
  7. 7.
    Iñesta, J.M., Buendía, M., Sarti, M.A.: Reliable polygonal approximations of imaged read objects though dominant point detection. Pattern Recognition 31, 685–697 (1998)CrossRefGoogle Scholar
  8. 8.
    Rico-Juan, J.R., Calera-Rubio, J.: Evaluation of handwritten character recognizers using tree-edit-distance and fast nearest neighbour search. In: Iñesta, J.M., Micó, L. (eds.) Pattern Recognition in Information Systems, Alicante (Spain), pp. 326–335. ICEIS Press (2002)Google Scholar
  9. 9.
    Serra, J.: Image Analysis and mathematical morphology. Academic Press, London (1982)MATHGoogle Scholar
  10. 10.
    Carrasco, R.C., Forcada, M.L.: A note on the Nagendraprasad-Wang-Gupta thinning algorithm. Pattern Recognition Letters 16, 539–541 (1995)MATHCrossRefGoogle Scholar
  11. 11.
    Ristad, E., Yianilos, P.: Learning string-edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 522–532 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Juan Ramón Rico-Juan
    • 1
  • José M. Iñesta
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain

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