Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data

Research output: Contribution to conferencePaperpeer-review

Standard Standard

Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data. / Hennessey, Sam; Williams, Frank; Kuncheva, Ludmila.
2024. Paper presented at 2024 IEEE 12th International Conference on Intelligent Systems (IS), Varna, Bulgaria.

Research output: Contribution to conferencePaperpeer-review

HarvardHarvard

Hennessey, S, Williams, F & Kuncheva, L 2024, 'Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data', Paper presented at 2024 IEEE 12th International Conference on Intelligent Systems (IS), Varna, Bulgaria, 29/08/24 - 31/08/24. <https://ieeexplore.ieee.org/abstract/document/10705263>

APA

Hennessey, S., Williams, F., & Kuncheva, L. (2024). Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data. Paper presented at 2024 IEEE 12th International Conference on Intelligent Systems (IS), Varna, Bulgaria. https://ieeexplore.ieee.org/abstract/document/10705263

CBE

Hennessey S, Williams F, Kuncheva L. 2024. Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data. Paper presented at 2024 IEEE 12th International Conference on Intelligent Systems (IS), Varna, Bulgaria.

MLA

Hennessey, Sam, Frank Williams, and Ludmila Kuncheva Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data. 2024 IEEE 12th International Conference on Intelligent Systems (IS), 29 Aug 2024, Varna, Bulgaria, Paper, 2024. 6 p.

VancouverVancouver

Hennessey S, Williams F, Kuncheva L. Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data. 2024. Paper presented at 2024 IEEE 12th International Conference on Intelligent Systems (IS), Varna, Bulgaria.

Author

Hennessey, Sam ; Williams, Frank ; Kuncheva, Ludmila. / Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data. Paper presented at 2024 IEEE 12th International Conference on Intelligent Systems (IS), Varna, Bulgaria.6 p.

RIS

TY - CONF

T1 - Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data

AU - Hennessey, Sam

AU - Williams, Frank

AU - Kuncheva, Ludmila

PY - 2024/8/29

Y1 - 2024/8/29

N2 - The identification of animals from video footage isan important ecological pursuit. It presents a labour-intensivetask, requiring experts to invest considerable time analysing videorecordings. While identifying humans from video is a mainstreamresearch quest, much less has been done for recognising animals’identities. This paper explores and contrasts the effectivenessof hierarchical and centroid-based constraint clustering methodsacross five manually annotated video datasets containing animals.We aim to determine the most suitable methodology for integrat-ing into a fully autonomous pipeline for animal re-identification.Our experimental findings indicate that, contrary to expectation,online hierarchical constraint clustering surpasses centroid-basedconstrained clustering.

AB - The identification of animals from video footage isan important ecological pursuit. It presents a labour-intensivetask, requiring experts to invest considerable time analysing videorecordings. While identifying humans from video is a mainstreamresearch quest, much less has been done for recognising animals’identities. This paper explores and contrasts the effectivenessof hierarchical and centroid-based constraint clustering methodsacross five manually annotated video datasets containing animals.We aim to determine the most suitable methodology for integrat-ing into a fully autonomous pipeline for animal re-identification.Our experimental findings indicate that, contrary to expectation,online hierarchical constraint clustering surpasses centroid-basedconstrained clustering.

KW - Constrained clustering

KW - Hierarchical Clustering

KW - Animal re-identification

KW - Pairwise constraints

KW - Semi-supervised learning

M3 - Paper

T2 - 2024 IEEE 12th International Conference on Intelligent Systems (IS)

Y2 - 29 August 2024 through 31 August 2024

ER -