Abstract
The nonlinear structure of the artificial neural network is efficient for the classification; however, the choice of features is a fundamental problem due to their direct impact on the network convergence and performance. In this paper, we present a new method of image classification method based on Legendre–Fourier moments using an artificial neural network. We used LFMs to extract features from images. In result, every image is represented by a descriptor vector; these vectors are inputs of our neural network. We tested this model on Fashion-MNIST database and we got important results; the model’s accuracy exceeds 97%. The validity of this proposed method has provided under different transformations.
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Machhour, A., El Mallahi, M., Lakhliai, Z., Tahiri, A., Chenouni, D. (2020). Image Classification Using Legendre–Fourier Moments and Artificial Neural Network. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_29
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DOI: https://doi.org/10.1007/978-981-15-0947-6_29
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