Advertisement

Identification of Diatoms by Grid Graph Matching

  • Stefan Fischer
  • Kaspar Gilomen
  • Horst Bunke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

Diatoms are unicellular algae found in water and other places wherever there is humidity and enough light for photo synthesis. In this paper a graph matching based identification approach for the retrieval of diatoms from an image database is presented. The retrieval is based on the matching of labeled grid graphs carrying texture information of the underlying diatom. A grid graph is a regular, rectangular arrangement of nodes overlaid on an image. Each node of the graph is labeled with texture features describing a rectangular sub-region of the object. Properties of gray level co-occurrence matrices as well as of Gabor wavelets are used as texture features. The method has been evaluated on a diatom database holding images of 188 different diatoms belonging to 38 classes. For the identification of these diatoms recognition rates of more than 90 percent were obtained.

Keywords

Recognition Rate Image Database Query Image Graph Match Gabor Wavelet 
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.

References

  1. 1.
    Automatic Diatom Identification And Classification. Project home page: http://www.ualg.pt/adiac/.
  2. 2.
    E. Bienenstock and C. von der Malsburg. A neural network for invariant pattern recognition. Europhysics Letters, 4:121–126, 1987.CrossRefGoogle Scholar
  3. 3.
    H. Bunke. Recent developments in graph matching. In Proceedings of the 15th International Conference on Pattern Recognition (ICPR’ 00), volume 2, pages 117–124, Barcelona, Spain, September 3–8 2000.Google Scholar
  4. 4.
    S. Fischer, M. Binkert, and H. Bunke. Feature based retrieval of diatoms in an image database using decision trees. In Proceedings of the 2nd International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2000), pages67–72, Baden-Baden, Germany, August 2000.Google Scholar
  5. 5.
    S. Fischer, M. Binkert, and H. Bunke. Symmetry based indexing of diatoms in an image database. In Proceedings of the 15th International Conference on Pattern Recognition (ICPR’ 00), volume 2, pages 899–902, Barcelona, Spain, September 3–8 2000.Google Scholar
  6. 6.
    K. Gilomen. Texture based identification of diatoms (in German). Master’s thesis, University of Bern, 2001.Google Scholar
  7. 7.
    R. M. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, pages 610–621, 1973.Google Scholar
  8. 8.
    A. K. Jain and F. Farrokhnia. Unsupervised texture segmentation using Gabor filters. Pattern Recognition, 24(12):1167–1186, 1991.CrossRefGoogle Scholar
  9. 9.
    M. Lades, J. Vorbrüggen, J. Buhmann, J. Lange, C. von der Malsburg, R. Würtz, and W. Konen. Distortion invariant object recognition in the dynamic link architecture. IEEE Transaction on Computers, 42(3):300–311, 1993.CrossRefGoogle Scholar
  10. 10.
    D. Mou and E. F. Stoermer. Separating tabellaria (bacillariophyceae) shape groups: A large sample approach based on fourier descriptor analysis. Journal of Phycology, 28:386–395, 1992.CrossRefGoogle Scholar
  11. 11.
    J. L. Pappas and E. F. Stoermer. Multidimensional analysis of diatom morphological phenotypic variation and relation to niche. Ecoscience, 2:357–367, 1995.Google Scholar
  12. 12.
    I. Pitas. Digital image processing algorithms. Prentice Hall, London, 1993.Google Scholar
  13. 13.
    E. F. Stoermer. A simple, but useful, application of image analysis. Journal of Paleolimnology, 15:111–113, 1996.CrossRefGoogle Scholar
  14. 14.
    E. F. Stoermer and J. P. Smol, editors. The Diatoms: Applications for the Environmental and Earth Science. Cambridge University Press, 1999.Google Scholar
  15. 15.
    A. Tefas, C. Kotropoulos, and I. Pitas. Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:735–746, 2001.CrossRefGoogle Scholar
  16. 16.
    M. Tuceryan and A. K. Jain. Texture analysis. In C. H. Chen, L. F. Pau, and P. S. P. Wang, editors, The Handbook of Pattern Recognition and Computer Vision, pages 207–248. World Scientific Publishing Co, 2 edition, 1998.Google Scholar
  17. 17.
    M. Wilkinson, J. Roerdink, S. Droop, and M. Bayer. Diatom contour analysis using morphological curvature scale spaces. In Proceedings of the 15th International Conference on Pattern Recognition (ICPR’ 00), pages 656–659, Barcelona, Spain, September 3–7 2000.Google Scholar
  18. 18.
    L. Wiskott, J. Fellous, N. Krüger, and C. von der Malsburg. Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):775–779, 1997.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Stefan Fischer
    • 1
  • Kaspar Gilomen
    • 1
  • Horst Bunke
    • 1
  1. 1.Institute of Computer Science and Applied MathematicsUniversity of BernSwitzerland

Personalised recommendations