TY - JOUR

T1 - Semi-Supervised Classification With Pairwise Constraints: A Case Study on Animal Identification from Video

AU - Kuncheva, Ludmila

AU - Garrido-Labrador, Jose

AU - Ramos-Perez, Ismael

AU - Hennessey, Samuel

AU - Rodriguez, Juan

PY - 2024/4/1

Y1 - 2024/4/1

N2 - 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 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.

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 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.

U2 - 10.1016/j.inffus.2023.102188

DO - 10.1016/j.inffus.2023.102188

M3 - Article

VL - 104

JO - Information Fusion

JF - Information Fusion

SN - 1566-2535

M1 - 102188

ER -