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ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis
Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images. The gap between human color perception, linguistic color terminology, and digital representation are the main challenges for developing methods that...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052715/ https://www.ncbi.nlm.nih.gov/pubmed/36992044 http://dx.doi.org/10.3390/s23063338 |
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author | Nicolás-Sáenz, Laura Ledezma, Agapito Pascau, Javier Muñoz-Barrutia, Arrate |
author_facet | Nicolás-Sáenz, Laura Ledezma, Agapito Pascau, Javier Muñoz-Barrutia, Arrate |
author_sort | Nicolás-Sáenz, Laura |
collection | PubMed |
description | Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images. The gap between human color perception, linguistic color terminology, and digital representation are the main challenges for developing methods that properly classify pixels based on color. To address these challenges, we propose a novel method combining geometric analysis, color theory, fuzzy color theory, and multi-label systems for the automatic classification of pixels into 12 conventional color categories, and the subsequent accurate description of each of the detected colors. This method presents a robust, unsupervised, and unbiased strategy for color naming, based on statistics and color theory. The proposed model, “ABANICCO” (AB ANgular Illustrative Classification of COlor), was evaluated through different experiments: its color detection, classification, and naming performance were assessed against the standardized ISCC–NBS color system; its usefulness for image segmentation was tested against state-of-the-art methods. This empirical evaluation provided evidence of ABANICCO’s accuracy in color analysis, showing how our proposed model offers a standardized, reliable, and understandable alternative for color naming that is recognizable by both humans and machines. Hence, ABANICCO can serve as a foundation for successfully addressing a myriad of challenges in various areas of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging. |
format | Online Article Text |
id | pubmed-10052715 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100527152023-03-30 ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis Nicolás-Sáenz, Laura Ledezma, Agapito Pascau, Javier Muñoz-Barrutia, Arrate Sensors (Basel) Article Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images. The gap between human color perception, linguistic color terminology, and digital representation are the main challenges for developing methods that properly classify pixels based on color. To address these challenges, we propose a novel method combining geometric analysis, color theory, fuzzy color theory, and multi-label systems for the automatic classification of pixels into 12 conventional color categories, and the subsequent accurate description of each of the detected colors. This method presents a robust, unsupervised, and unbiased strategy for color naming, based on statistics and color theory. The proposed model, “ABANICCO” (AB ANgular Illustrative Classification of COlor), was evaluated through different experiments: its color detection, classification, and naming performance were assessed against the standardized ISCC–NBS color system; its usefulness for image segmentation was tested against state-of-the-art methods. This empirical evaluation provided evidence of ABANICCO’s accuracy in color analysis, showing how our proposed model offers a standardized, reliable, and understandable alternative for color naming that is recognizable by both humans and machines. Hence, ABANICCO can serve as a foundation for successfully addressing a myriad of challenges in various areas of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging. MDPI 2023-03-22 /pmc/articles/PMC10052715/ /pubmed/36992044 http://dx.doi.org/10.3390/s23063338 Text en © 2023 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 Nicolás-Sáenz, Laura Ledezma, Agapito Pascau, Javier Muñoz-Barrutia, Arrate ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis |
title | ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis |
title_full | ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis |
title_fullStr | ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis |
title_full_unstemmed | ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis |
title_short | ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis |
title_sort | abanicco: a new color space for multi-label pixel classification and color analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052715/ https://www.ncbi.nlm.nih.gov/pubmed/36992044 http://dx.doi.org/10.3390/s23063338 |
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