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.pdf - Whole Document Available under License Creative Commons Attribution 4.0 International. 4MB |
Item Type: | Article |
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Item Status: | Live Archive |
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 |
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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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