Ultrametrics for context-aware comparison of binary images

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dc.contributor.authorLópez Molina, Carlos
dc.contributor.authorIglesias Rey, Sara
dc.contributor.authorBaets, Bernard de
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.date.accessioned2024-03-25T17:08:06Z
dc.date.available2024-03-25T17:08:06Z
dc.date.issued2024
dc.date.updated2024-03-25T16:52:48Z
dc.description.abstractQuantitative image comparison has been a key topic in the image processing literature for the past 30 years. The reasons for it are diverse, and so is the range of applications in which measures of comparison are needed. Examples of image processing tasks requiring such measures are the evaluation of algorithmic results (through the comparison of computer-generated results to given ground truth) or the selection of loss/goal functions in a machine learning context. Measures of comparison in literature take different inspirations, and are often tailored to specific needs. Nevertheless, even if some measures of comparison intend to replicate how humans evaluate the similarity of two images, they normally overlook a fundamental characteristic of the way humans perform such evaluation: the context of comparison. In this paper, we present a measure of comparison for binary images that incorporates a sense of context. More specifically, we present a Methodology for the generation of ultrametrics for context-aware comparison of binary images. We test our proposal in the context of boundary image comparison on the BSDS500 benchmark.en
dc.description.sponsorshipThe authors gratefully acknowledge the financial support of the Spanish Research Agency, project PID2019-108392GB-I00 (AEI/10. 13039/501100011033), as well as that of Navarra de Servicios y Tecnologías, S.A. (NASERTIC).en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationLopez-Molina, C., Iglesias-Rey, S., De Baets, B. (2024) Ultrametrics for context-aware comparison of binary images. Information Fusion, 103, 1-12. https://doi.org/10.1016/j.inffus.2023.102101.en
dc.identifier.doi10.1016/j.inffus.2023.102101
dc.identifier.issn1566-2535
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/47789
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofInformation Fusion 103 (2024) 102101en
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00/ES/
dc.relation.publisherversionhttps://doi.org/10.1016/j.inffus.2023.102101
dc.rights© 2023 The Authors. This is an open access article under the CC BY-NC license.en
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectBinary imageen
dc.subjectContext awarenessen
dc.subjectImage comparisonen
dc.subjectUltrametricen
dc.titleUltrametrics for context-aware comparison of binary imagesen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublicationb1df82f9-2ce4-488f-afe0-98e7f27ece58
relation.isAuthorOfPublication0f73d493-c4ef-48af-a54b-3490115dcd2b
relation.isAuthorOfPublication.latestForDiscoveryb1df82f9-2ce4-488f-afe0-98e7f27ece58

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