Abstract
In this paper the results in leaves classification with non-parametrized one nearest neighbor and multilayer perceptron classifiers are presented. The feature vectors are composed of Fourier descriptors that are calculated for leaves contours with fast adaptive Fourier transform algorithm. An application of fast adaptive algorithm results in new fast adaptive Fourier descriptors.
Experimental results prove that the fast adaptive Fourier transform algorithm significantly accelerates the process of descriptors calculation and enables almost eightfold reduction in the number of contour data with no effect on classification performance. Moreover the neural network classifier gives higher accuracies of classification in comparison to the minimum distance one nearest neighbor classifier.
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Puchala, D., Yatsymirskyy, M. (2008). Neural Network in Fast Adaptive Fourier Descriptor Based Leaves Classification. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_14
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DOI: https://doi.org/10.1007/978-3-540-69731-2_14
Publisher Name: Springer, Berlin, Heidelberg
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