Object Recognition Using Enhanced Particle Swarm Optimization

Willis, Michael, Zhang, Li, Liu, Han, Xie, Hailun and Mistry, Kamlesh (2020) Object Recognition Using Enhanced Particle Swarm Optimization. In: 2020 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, Piscataway, NJ, pp. 241-246. ISBN 9780738124261

WILLIS 2020 Object recognition (AAM).pdf - Accepted Version

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Official URL: https://doi.org/10.1109/ICMLC51923.2020.9469584


The identification of the most discriminative features in an explainable AI decision-making process is a challenging problem. This research tackles such challenges by proposing Particle Swarm Optimization (PSO) variants embedded with novel mutation and sampling iteration operations for feature selection in object recognition. Specifically, five PSO variants integrating different mutation and sampling strategies have been proposed to select the most discriminative feature subsets for the classification of different objects. A mutation strategy is firstly proposed by randomly flipping the particle positions in some dimensions to generate new feature interactions. Moreover, instead of embarking the position updating evolution in PSO, the proposed PSO variants generate offspring solutions through a sampling mechanism during the initial search process. Two offspring generation sampling schemes are investigated, i.e. the employment of the personal and global best solutions obtained using the mutation mechanism, respectively, as the starting positions for the subsequent search process. Subsequently, several machine learning algorithms are used in conjunction with the proposed PSO variants to perform object classification. As evidenced by the empirical results, the proposed PSO variants outperform the original PSO algorithm, significantly, for feature optimization.

Item Type: Book Section
Additional Information: 19th International Conference on Machine Learning and Cybernetics, ICMLC 2020 4/12/20 → … Virtual, Online
Uncontrolled Keywords: Feature selection, Object recognition, Optimization
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 10 May 2022 13:01
Last Modified: 10 May 2022 13:15
URI: http://nrl.northumbria.ac.uk/id/eprint/49083

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