Improving Spider Recognition Based on Biometric Web Analysis

  • Carlos M. Travieso Gonzalez
  • Jaime Roberto Ticay-Rivas
  • Marcos del Pozo-Baños
  • William G. Eberhard
  • Jesús B. Alonso-Hernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

This work presents an improvement of the automatic and supervised spider identification approach based on biometric spider web analysis. We have used as feature extractor, a Joint Approximate Diagonalization of Eigen-matrixes Independent Component Analysis applying to a binary image with a reduced size (20×20 pixels) from the colour original image. Finally, we have applied a least square support vector machine as classifier, reaching over 98.15% in our hold-50%-out validation. This system is making easier Biologists’ tasks in this field, because they can have a second opinion or have a tool for this work.

Keywords

Spider webs spider classification independent component analysis support vector machine 

References

  1. 1.
    Sytnik, K.M.: Preservation of biological diversity: Top-priority tasks of society and state. Ukrainian Journal of Physical Optics 11(suppl. 1), 2–10 (2010)CrossRefGoogle Scholar
  2. 2.
    Carvalho, J.C., Cardoso, P., Crespo, L.C., Henriques, S., Carvalho, R., Gomes, P.: Biogeographic patterns of spiders in coastal dunes along a gradient of mediterraneity. Biodiversity and Conservation, 1–22 (2011)Google Scholar
  3. 3.
    Johnston, J.M.: The contribution of microarthropods to aboveground food webs: A review and model of belowground transfer in a coniferous forest. American Midland Naturalist 143, 226–238 (2000)CrossRefGoogle Scholar
  4. 4.
    Peterson, A.T., Osborne, D.R., Taylor, D.H.: Tree trunk arthropod faunas as food resources for birds. Ohio Journal of Science 89(1), 23–25 (1989)Google Scholar
  5. 5.
    Cardoso, P., Arnedo, M.A., Triantis, K.A., Borges, P.A.V.: Drivers of diversity in Macaronesian spiders and the role of species extinctions. J. Biogeogr. 37, 1034–1046 (2010)CrossRefGoogle Scholar
  6. 6.
    Finch, O.D., Blick, T., Schuldt, A.: Macroecological patterns of spider species richness across Europe. Biodivers. Conserv. 17, 2849–2868 (2008)CrossRefGoogle Scholar
  7. 7.
    Eberhard, W.G.: Behavioral Characters for the Higher Classification of Orb-Weaving Spiders. Evolution, Society for the Study of Evolution 36(5), 1067–1095 (1982)Google Scholar
  8. 8.
    Eberhard, W.G.: Early Stages of Orb Construction by Philoponella Vicina, Leucauge Mariana, and Nephila Clavipes (Araneae, Uloboridae and Tetragnathidae), and Their Phylogenetic Implications. Journal of Arachnology, American Arachnological Society 18(2), 205–234 (1990)Google Scholar
  9. 9.
    Eberhard, W.G.: Computer Simulation of Orb-Web Construction. J. American Zoologist, 229–238 (1969)Google Scholar
  10. 10.
    Suresh, P.B., Zschokke, S.: A computerised method to observe spider web building behaviour in a semi-natural light environment. In: 19th European Colloquium of Arachnology, Denmark (2000)Google Scholar
  11. 11.
    Ticay-Rivas, J.R., del Pozo-Baños, M., Eberhard, W.G., Alonso, J.B., Travieso, C.M.: Spider Recognition by Biometric Web Analysis. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part II. LNCS, vol. 6687, pp. 409–417. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Otsu, N.: A thresholding selection method from gray-level histogram. IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing. Pearson Prentice Hall (2003)Google Scholar
  14. 14.
    Hyvärinen, A.: Independent Component Analysis: Algorithms and Applications. Neural Networks 13(4-5), 411–430 (2000)CrossRefGoogle Scholar
  15. 15.
    Schölkopf, B., Smola, A.J.: Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press (2002)Google Scholar
  16. 16.
    Vapnik, V.: The Nature of Statistical learning Theory. Springer, New York (1995)MATHGoogle Scholar
  17. 17.
    Yan, F., Qiang, Y., Ruixiang, S., Dequan, L., Rong, Z., Ling, C.X., Wen, G.: Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry. Bioinformatics 20(12), 1948–1954 (2004)CrossRefGoogle Scholar
  18. 18.
    Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Carlos M. Travieso Gonzalez
    • 1
  • Jaime Roberto Ticay-Rivas
    • 1
  • Marcos del Pozo-Baños
    • 1
  • William G. Eberhard
    • 2
  • Jesús B. Alonso-Hernández
    • 1
  1. 1.Signals and Communications Department, Institute for Technological Development and Innovation in CommunicationsUniversity of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain
  2. 2.Smithsonian Tropical Research Institute and Escuela de BiologiaUniversidad de Costa Rica, Ciudad UniversitariaCosta Rica

Personalised recommendations