Zur Kurzanzeige

dc.contributor.authorKuncheva, Ludmila I. .
dc.contributor.authorGarrido Labrador, José Luis 
dc.contributor.authorRamos Pérez, Ismael 
dc.contributor.authorHennessey, Samuel L.
dc.contributor.authorRodríguez Diez, Juan José 
dc.date.accessioned2023-12-14T09:32:56Z
dc.date.available2023-12-14T09:32:56Z
dc.date.issued2023-12
dc.identifier.issn1566-2535
dc.identifier.urihttp://hdl.handle.net/10259/8203
dc.description.abstractMainstream semi-supervised classification assumes that part of the available data are labelled. Here we assume that, in addition to the labels, we have pairwise constraints on the unlabelled data. Each constraint links two instances, and is one of Must Link (ML, belong to the same class) or Cannot Link (CL, belong to different classes). We propose an approach that uses the labelled data to train a classifier and then applies the ML and CL constraints in subsequent labelling. In our approach, a set of instances are labelled at the same time. Our case study is on animal re-identification. The dataset consists of five free-camera video clips of animals (koi fish, pigeons and pigs), annotated with bounding boxes and animal identities. The proposed approach combines the representations or classifiers predictions from the bounding boxes of consecutive frames. We demonstrate that our approach outperforms standard classifiers, constrained clustering, as well as inductive and transductive semi-supervised learning, using five feature representations.en
dc.description.sponsorshipThis work is supported by the UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC), funded by grant EP/S023992/1. This work is also supported by the Junta de Castilla León under project BU055P20 (JCyL/FEDER, UE), and the Ministry of Science and Innovation of Spain under the projects PID2020-119894GB-I00/AEI/10.13039/501100011033, co-financed through European Union FEDER funds. J.L. Garrido-Labrador is supported through Consejería de Educación of the Junta de Castilla y León and the European Social Fund through a pre-doctoral grant (EDU/875/2021). I. Ramos-Perez is supported by the predoctoral grant (BDNS 510149) awarded by the Universidad de Burgos, Spain. J.J. Rodríguez was supported by mobility grant PRX21/00638 of the Spanish Ministry of Universities .en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofInformation Fusion. 2023, 102188en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAnimal re-identificationen
dc.subjectComputer visionen
dc.subjectClassificationen
dc.subjectSemi-supervised learningen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.subject.otherBiologíaes
dc.subject.otherBiologyen
dc.titleSemi-supervised classification with pairwise constraints: A case study on animal identification from videoen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1016/j.inffus.2023.102188es
dc.identifier.doi10.1016/j.inffus.2023.102188
dc.relation.projectIDinfo:eu-repo/grantAgreement/UKRI//EP%2FS023992%2F1/GB/en
dc.relation.projectIDinfo:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20//Métodos y Aplicaciones Industriales del Aprendizaje Semisupervisado/es
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119894GB-I00/ES/APRENDIZAJE AUTOMATICO CON DATOS ESCASAMENTE ETIQUETADOS PARA LA INDUSTRIA 4.0/es
dc.relation.projectIDinfo:eu-repo/grantAgreement/MIU//PRX21%2F00638/ES/es
dc.journal.titleInformation Fusionen
dc.page.initial102188es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


Dateien zu dieser Ressource

Thumbnail

Das Dokument erscheint in:

Zur Kurzanzeige