Automatic Identification of Diatoms Using Decision Forests

  • Stefan Fischer
  • Horst Bunke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2123)


A feature based identification scheme for microscopic images of diatoms is presented in this paper. Diatoms are unicellular algae found in water and other places wherever there is humidity and enough light for photo synthesis. The proposed automatic identification scheme follows a decision tree based classification approach. In this paper two different ensemble learning methods are evaluated and results are compared with those of single decision trees. As test sets two different diatom image databases are used. For each image in the databases general features like symmetry, geometric properties, moment invariants, and Fourier descriptors as well as diatom specific features like striae density and direction are computed.


Decision Tree Recognition Rate Moment Invariant Fourier Descriptor Random Subspace 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

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

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