Abstract
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.
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Fischer, S., Bunke, H. (2001). Automatic Identification of Diatoms Using Decision Forests. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2001. Lecture Notes in Computer Science(), vol 2123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44596-X_14
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DOI: https://doi.org/10.1007/3-540-44596-X_14
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