Improving the Machine Learning Performance for Image Recognition Using a New Set of Mountain Fourier Moments


  • Yahya Sahmoudi EST, Sidi Mohamed Ben Abdellah University
  • Omar el Ogri
  • Jaouad el Mekkaoui
  • Boujamaa Janati Idrissi
  • Amal Hjouji



Multichannel invariant moments, Pattern recognition, Orthogonal Mountain functions, Mountain-Fourier invariant moments., K-nearest neighbours (KNN), support vector machine (SVM)


The orthogonal moments are giving relevant results of these last years within the framework of object detection, pattern recognition and image reconstruction. This article is based on orthogonal functions called "Orthogonal Mountain functions (OMFs)" and we introduce a new set of moments called the multichannel Mountain Fourier moments (MMFMs), their performance is in reconstruction, noise invariants, rotation, scale and translation for image color. To validate these proposed techniques, we made several experimental tests to analyse images. We compare the results obtained from invariant moments and other current orthogonal invariant moments; the experiments show the power of the proposed moments.


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2024-04-03 — Updated on 2024-04-05

How to Cite

Sahmoudi, Y., el Ogri, O., el Mekkaoui, J., Janati Idrissi, B., & Hjouji, A. (2024). Improving the Machine Learning Performance for Image Recognition Using a New Set of Mountain Fourier Moments. Image Analysis and Stereology, 43(1), 67–84.



Original Research Paper