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

Hussein Samma; Chee Peng Lim; Junita Mohamad Saleh; Shahrel Azmin Suandi

doi:10.1007/s12293-016-0187-0

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

Hussein Samma, Chee Peng Lim^*, Junita Mohamad Saleh, Shahrel Azmin Suandi

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

28 Scopus citations

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.

Original language	English
Pages (from-to)	235-251
Number of pages	17
Journal	Memetic Computing
Volume	8
Issue number	3
DOIs	https://doi.org/10.1007/s12293-016-0187-0
State	Published - 1 Sep 2016
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2016, Springer-Verlag Berlin Heidelberg.

Keywords

Fuzzy support vector machine
Licence plate recognition
Memetic particle swarm optimization

ASJC Scopus subject areas

General Computer Science
Control and Optimization

Access to Document

10.1007/s12293-016-0187-0

Cite this

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

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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AU - Samma, Hussein

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PY - 2016/9/1

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N2 - 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.

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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