A memetic-based fuzzy support vector machine model and its application to license plate recognition

In this paper, a novel fuzzy support vector machine (FSVM) coupled with a memetic particle swarm optimization (MPSO) algorithm is introduced. Its application to a license plate recognition problem is studied comprehensively. The proposed recognition model comprises linear FSVM classifiers which are...

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Bibliographic Details
Published in:Memetic computing 2016-09, Vol.8 (3), p.235-251
Main Authors: Samma, Hussein, Lim, Chee Peng, Saleh, Junita Mohamad, Suandi, Shahrel Azmin
Format: Article
Language:English
Subjects:
Algorithms
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Character recognition
Complex Systems
Control
Engineering
Global optimization
Licenses
Local optimization
Mathematical and Computational Engineering
Mechatronics
Particle swarm optimization
Regular Research Paper
Robotics
Support vector machines
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Summary:In this paper, a novel fuzzy support vector machine (FSVM) coupled with a memetic particle swarm optimization (MPSO) algorithm is introduced. Its application to a license plate recognition problem is studied comprehensively. The proposed recognition model comprises linear FSVM classifiers which are used to locate a two-character window of the license plate. A new MPSO algorithm which consists of three layers i.e. a global optimization layer, a component optimization layer, and a local optimization layer is constructed. During the construction process, MPSO performs FSVM parameters tuning, feature selection, and training instance selection simultaneously. A total of 220 real Malaysian car plate images are used for evaluation. The experimental results indicate the effectiveness of the proposed model for undertaking license plate recognition problems.
ISSN:1865-9284
1865-9292
DOI:10.1007/s12293-016-0187-0