Publication:
ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis

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dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: BSEL - Laboratorio de Ciencia e Ingeniería Biomédicaen
dc.contributor.authorNicolás-Sáenz, Laura
dc.contributor.authorLedezma Espino, Agapito Ismael
dc.contributor.authorPascau González-Garzón, Javier
dc.contributor.authorMuñoz Barrutia, María Arrate
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es
dc.date.accessioned2023-06-27T12:42:21Z
dc.date.available2023-06-27T12:42:21Z
dc.date.issued2023-03-02
dc.description.abstractClassifying 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.en
dc.description.sponsorshipThis research was funded by the Ministerio de Ciencia, Innovacción y Universidades, Agencia Estatal de Investigación, under grant PID2019-109820RB, MCIN/AEI/10.13039/501100011033 co-financed by the European Regional Development Fund (ERDF) "A way of making Europe" to A.M.-B. and L.N.-S.en
dc.description.statusPublicadoes
dc.format.extent23es
dc.identifier.bibliographicCitationSensors, (2023), 23(6), 3338, (pp:1-23)
dc.identifier.doihttps://doi.org/10.3390/s23063338
dc.identifier.issn1424-3210
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue6, 3338es
dc.identifier.publicationlastpage23es
dc.identifier.publicationtitleSensorses
dc.identifier.publicationvolume23es
dc.identifier.urihttps://hdl.handle.net/10016/37642
dc.identifier.uxxiAR/0000033093
dc.publisherMDPIen
dc.relation.projectIDGobierno de España. PID2019-109820RB-I00es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights© 1996-2023 MDPI (Basel, Switzerland)en
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaBiología y Biomedicinaes
dc.subject.otherimage color analysisen
dc.subject.otherimage analysisen
dc.subject.othersemanticsen
dc.subject.otherfuzzy color spaceen
dc.subject.othercolor modelingen
dc.subject.othercolor segmentationen
dc.subject.othercolor classificationen
dc.subject.otherhuman perceptionen
dc.titleABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysisen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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