Abstract

This paper presents a novel 2D shape classification approach, which exploits in this context the huge amount of work carried out by bioinformaticians in the biological sequence analysis research field. In particular, in the approach presented here, we propose to encode shapes as biological sequences, employing the widely known sequence alignment tool called BLAST (Basic Local Alignment Search Tool) to devise a similarity score, used in a nearest neighbour scenario. Obtained results on standard datasets show the feasibility of the proposed approach.

Keywords

2D shape classification sequence alignment biological sequences 

References

  1. 1.
    Loncaric, S.: A survey of shape analysis techniques. Pattern Recognition 31(8), 983–1001 (1998)CrossRefGoogle Scholar
  2. 2.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)MATHCrossRefGoogle Scholar
  3. 3.
    Mingqiang, Y., Kidiyo, K., Joseph, R.: A survey of shape feature extraction techniques. In: Yin, P.Y. (ed.) Pattern Recognition Techniques, Technology and Applications (2008)Google Scholar
  4. 4.
    Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge Univ. (1998)Google Scholar
  5. 5.
    Baldi, P., Brunak, S.: Bioinformatics: the Machine Learning Approach, 2nd edn. MIT Press (2001)Google Scholar
  6. 6.
    Madeira, S., Oliveira, A.: Biclustering algorithms for biological data analysis: a survey. IEEE Trans. on Computational Biology and Bioinformatics 1, 24–44 (2004)CrossRefGoogle Scholar
  7. 7.
    Bronstein, A., Bronstein, M., Kimmel, R.: The video genome. arXiv:1003.5320v1 (2010)Google Scholar
  8. 8.
    Altschul, S., Gish, W., Miller, W., Myers, E., Lipman, D.: Basic local alignment search tool. Journal of Molecular Biology 215, 403–410 (1990)Google Scholar
  9. 9.
    Andreu, G., Crespo, A., Valiente, J.: Selecting the toroidal self-organizing feature maps (TSOFM) best organized to object recognition. In: Proc. of IEEE ICNN 1997, vol. 2, pp. 1341–1346 (1997)Google Scholar
  10. 10.
    Thakoor, N., Gao, J., Jung, S.: Hidden markov model-based weighted likelihood discriminant for 2-d shape classification. IEEE Transactions on Image Processing 16(11), 2707–2719 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Li, H., Homer, N.: A survey of sequence alignment algorithms for next-generation sequencing. Briefings in Bioinformatics 11(5), 473–483 (2010)CrossRefGoogle Scholar
  12. 12.
    Kemena, C., Notredame, C.: Upcoming challenges for multiple sequence alignment methods in the high-throughput era. Bioinformatics 25(19) (2009)Google Scholar
  13. 13.
    Notredame, C.: Recent evolutions of multiple sequence alignment algorithms. PLoS Computational Biology 3(8) (2007)Google Scholar
  14. 14.
    Needleman, S., Wunsch, C.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Modelecular Biology 48(3), 443–453 (1970)CrossRefGoogle Scholar
  15. 15.
    Smith, T., Waterman, M.: Identification of common molecular subsequences. Journal of Molecular Biology 147, 195–197 (1981)CrossRefGoogle Scholar
  16. 16.
    Altschul, S., Madden, T., Schaffer, A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.: Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Research 25, 3389–3402 (1997)CrossRefGoogle Scholar
  17. 17.
    Bergman, N.: Comparative Genomics, vol. 1 and 2. Humana Press (2007)Google Scholar
  18. 18.
    Gonzalez, R., Woods, R.: Digital Image Processing, 2nd edn. Prentice Hall (2002)Google Scholar
  19. 19.
    Mollineda, R., Vidal, E., Casacuberta, F.: Cyclic sequence alignments: Approximate versus optimal techniques. Int. Journal of Pattern Recognition and Artificial Intelligence 16(3), 291–299 (2002)CrossRefGoogle Scholar
  20. 20.
    Bicego, M., Martins, A., Murino, V., Aguiar, P., Figueiredo, M.: 2d shape recognition using information theoretic kernels. In: Proc. Int. Conf on Pattern Recognition, pp. 25–28 (2010)Google Scholar
  21. 21.
    Daliri, M., Torre, V.: Shape recognition based on kernel-edit distance. Computer Vision and Image Understanding 114(10), 1097–1103 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pietro Lovato
    • 1
  • Manuele Bicego
    • 1
  1. 1.Computer Science DepartmentUniversity of VeronaItaly

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