Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition

Tehsin, Sara, Rehman, Saad, Saeed, Muhammad Omer Bin , Riaz, Farhan, Hassan, Ali, Abbas, Muhammad, Young, Rupert and Alam, Mohammad S (2017) Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition. IEEE Access, 5 . pp. 24495-24502. ISSN 2169-3536

Full content URL: https://doi.org/10.1109/ACCESS.2017.2762354

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Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition
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Abstract

Advanced correlation filters are an effective tool for target detection within a particular class. Most correlation filters are derived from a complex filter equation leading to a closed form filter solution. The response of the correlation filter depends upon the selected values of the optimal trade-off (OT) parameters. In this paper, the OT parameters are optimized using particle swarm optimization with respect to two different cost functions. The optimization has been made generic and is applied to each target separately in order to achieve the best possible result for each scenario. The filters obtained using standard particle swarm optimization (PSO) and hierarchal particle swarm optimization algorithms have been compared for various test images with the filter solutions available in the literature. It has been shown that optimization improves the performance of the filters significantly.

Keywords:computer science, Correlation, Optimization, Particle swarm optimization, Mathematical model, Distortion, Convergence, Clustering algorithms
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:52388
Deposited On:16 Nov 2022 14:07

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