RT info:eu-repo/semantics/article
T1 Semi-supervised classification with pairwise constraints: A case study on animal identification from video
A1 Kuncheva, Ludmila I. .
A1 Garrido Labrador, José Luis
A1 Ramos Pérez, Ismael
A1 Hennessey, Samuel L.
A1 Rodríguez Diez, Juan José
K1 Animal re-identification
K1 Computer vision
K1 Classification
K1 Semi-supervised learning
K1 Informática
K1 Computer science
K1 Biología
K1 Biology
AB 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.
PB Elsevier
SN 1566-2535
YR 2023
FD 2023-12
LK http://hdl.handle.net/10259/8203
UL http://hdl.handle.net/10259/8203
LA eng
NO 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 .
DS Repositorio Institucional de la Universidad de Burgos
RD 08-may-2024