Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Lhermitte, Emma, Hilal, Mirvana, Furlong, Ryan, O’Brien, Vincent, Humeau-Heurtier, Anne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784836404804583424
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
work_keys_str_mv AT lhermitteemma deeplearningandentropybasedtexturefeaturesforcolorimageclassification
AT hilalmirvana deeplearningandentropybasedtexturefeaturesforcolorimageclassification
AT furlongryan deeplearningandentropybasedtexturefeaturesforcolorimageclassification
AT obrienvincent deeplearningandentropybasedtexturefeaturesforcolorimageclassification
AT humeauheurtieranne deeplearningandentropybasedtexturefeaturesforcolorimageclassification