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

• 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
• ISSN: 1865-9284; 1865-9292
• DOI: 10.1007/s12293-016-0187-0

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.