Image Classification Using Legendre–Fourier Moments and Artificial Neural Network

  • Abderrahmane MachhourEmail author
  • Mostafa El Mallahi
  • Zakia Lakhliai
  • Ahmed Tahiri
  • Driss Chenouni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1076)


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.


Image classification Legendre–Fourier moments Artificial neural network Features Descriptor vector Moment invariants 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Abderrahmane Machhour
    • 1
    Email author
  • Mostafa El Mallahi
    • 1
  • Zakia Lakhliai
    • 1
  • Ahmed Tahiri
    • 1
  • Driss Chenouni
    • 1
  1. 1.Sidi Mohamed Ben Abdellah University, Laboratory of Computer Science and Interdisciplinary Physics LIPI, ENSFezMorocco

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