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

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Abstract

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.

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Authors and Affiliations

  1. Imaging and Computational Intelligence Group (ICI), School of Electrical and Electronic Engineering, University of Science Malaysia, Penang, Malaysia

    Hussein Samma, Junita Mohamad Saleh & Shahrel Azmin Suandi

  2. Faculty of Education, University of Aden, Shabowah, Yemen

    Hussein Samma

  3. Institute for Intelligent Systems Research and Innovation, Deakin University, Victoria, Australia

    Chee Peng Lim

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  1. Hussein Samma
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  2. Chee Peng Lim
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  3. Junita Mohamad Saleh
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  4. Shahrel Azmin Suandi
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Correspondence to Chee Peng Lim.

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Samma, H., Lim, C.P., Saleh, J.M. et al. A memetic-based fuzzy support vector machine model and its application to license plate recognition. Memetic Comp. 8, 235–251 (2016). https://doi.org/10.1007/s12293-016-0187-0

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  • DOI: https://doi.org/10.1007/s12293-016-0187-0

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