2026-09-13T23:45:20Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/82032023-12-15T01:05:14Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Kuncheva, Ludmila I. . Garrido Labrador, José Luis Ramos Pérez, Ismael Hennessey, Samuel L. Rodríguez Diez, Juan José 2023-12-14T09:32:56Z 2023-12-14T09:32:56Z 2023-12 1566-2535 http://hdl.handle.net/10259/8203 10.1016/j.inffus.2023.102188 Mainstream 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. This 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 . application/pdf eng Elsevier Information Fusion. 2023, 102188 https://doi.org/10.1016/j.inffus.2023.102188 info:eu-repo/grantAgreement/UKRI//EP%2FS023992%2F1/GB/ info:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20//Métodos y Aplicaciones Industriales del Aprendizaje Semisupervisado/ info: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/ info:eu-repo/grantAgreement/MIU//PRX21%2F00638/ES/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Animal re-identification Computer vision Classification Semi-supervised learning Informática Biología Computer science Biology Semi-supervised classification with pairwise constraints: A case study on animal identification from video info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Information Fusion 102188