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2021 | OriginalPaper | Chapter

Enhancing the Performance of Grayscale Image Classification by 2D Charlier Moments Neural Networks

Authors : Zouhir Lakhili, Abdelmajid El Alami, Hassan Qjidaa

Published in: Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems

Publisher: Springer Singapore

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Abstract

This paper presents a new model for 2D image classification based on 2D discrete Charlier moments and neural networks to enhance the classification accuracy of Grayscale images. Discrete Charlier moments have the ability to extract relevant features from an image even in lower orders, and with high efficiency of the neural networks; we can design the proposed efficient model. Experiments are carried out on Coil-20 and ORL datasets to demonstrate the performance of the proposed model. The obtained results show the capability of the proposed model to achieve high classification accuracy on both datasets, and to outperform other recent methods.

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Metadata
Title
Enhancing the Performance of Grayscale Image Classification by 2D Charlier Moments Neural Networks
Authors
Zouhir Lakhili
Abdelmajid El Alami
Hassan Qjidaa
Copyright Year
2021
Publisher
Springer Singapore
Book
Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems

Print ISBN: 978-981-15-6258-7

Electronic ISBN: 978-981-15-6259-4

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-6259-4_14