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Deep Learning and Entropy-Based Texture Features for Color Image Classification
In the domain of computer vision, entropy—defined as a measure of irregularity—has been proposed as an effective method for analyzing the texture of images. Several studies have shown that, with specific parameter tuning, entropy-based approaches achieve high accuracy in terms of classification resu...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9688970/ https://www.ncbi.nlm.nih.gov/pubmed/36359667 http://dx.doi.org/10.3390/e24111577 |
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author | Lhermitte, Emma Hilal, Mirvana Furlong, Ryan O’Brien, Vincent Humeau-Heurtier, Anne |
author_facet | Lhermitte, Emma Hilal, Mirvana Furlong, Ryan O’Brien, Vincent Humeau-Heurtier, Anne |
author_sort | Lhermitte, Emma |
collection | PubMed |
description | In the domain of computer vision, entropy—defined as a measure of irregularity—has been proposed as an effective method for analyzing the texture of images. Several studies have shown that, with specific parameter tuning, entropy-based approaches achieve high accuracy in terms of classification results for texture images, when associated with machine learning classifiers. However, few entropy measures have been extended to studying color images. Moreover, the literature is missing comparative analyses of entropy-based and modern deep learning-based classification methods for RGB color images. In order to address this matter, we first propose a new entropy-based measure for RGB images based on a multivariate approach. This multivariate approach is a bi-dimensional extension of the methods that have been successfully applied to multivariate signals (unidimensional data). Then, we compare the classification results of this new approach with those obtained from several deep learning methods. The entropy-based method for RGB image classification that we propose leads to promising results. In future studies, the measure could be extended to study other color spaces as well. |
format | Online Article Text |
id | pubmed-9688970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96889702022-11-25 Deep Learning and Entropy-Based Texture Features for Color Image Classification Lhermitte, Emma Hilal, Mirvana Furlong, Ryan O’Brien, Vincent Humeau-Heurtier, Anne Entropy (Basel) Article In the domain of computer vision, entropy—defined as a measure of irregularity—has been proposed as an effective method for analyzing the texture of images. Several studies have shown that, with specific parameter tuning, entropy-based approaches achieve high accuracy in terms of classification results for texture images, when associated with machine learning classifiers. However, few entropy measures have been extended to studying color images. Moreover, the literature is missing comparative analyses of entropy-based and modern deep learning-based classification methods for RGB color images. In order to address this matter, we first propose a new entropy-based measure for RGB images based on a multivariate approach. This multivariate approach is a bi-dimensional extension of the methods that have been successfully applied to multivariate signals (unidimensional data). Then, we compare the classification results of this new approach with those obtained from several deep learning methods. The entropy-based method for RGB image classification that we propose leads to promising results. In future studies, the measure could be extended to study other color spaces as well. MDPI 2022-10-31 /pmc/articles/PMC9688970/ /pubmed/36359667 http://dx.doi.org/10.3390/e24111577 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lhermitte, Emma Hilal, Mirvana Furlong, Ryan O’Brien, Vincent Humeau-Heurtier, Anne Deep Learning and Entropy-Based Texture Features for Color Image Classification |
title | Deep Learning and Entropy-Based Texture Features for Color Image Classification |
title_full | Deep Learning and Entropy-Based Texture Features for Color Image Classification |
title_fullStr | Deep Learning and Entropy-Based Texture Features for Color Image Classification |
title_full_unstemmed | Deep Learning and Entropy-Based Texture Features for Color Image Classification |
title_short | Deep Learning and Entropy-Based Texture Features for Color Image Classification |
title_sort | deep learning and entropy-based texture features for color image classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9688970/ https://www.ncbi.nlm.nih.gov/pubmed/36359667 http://dx.doi.org/10.3390/e24111577 |
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