Enhanced particle swarm optimization-based models and their application to license plate recognition
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Date
2016-02-01
Authors
Hussein Salem Ali Samma
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Abstract
Pattern recognition models play an important role in many real-world applications
such as text detection and object recognition. Numerous methodologies including
Computational Intelligence (CI) models have been developed in the literature to
tackle image-based pattern recognition problems. Focused on CI models, this
research presents efficient Particle Swarm Optimization (PSO)-based models and
their application to license plate recognition. Firstly, a new Reinforcement Learningbased
Memetic Particle Swarm Optimization (RLMPSO) model is introduced. To
assess the performance of RLMPSO, benchmark optimization problems are
employed, and the bootstrap method is used to quantify the results statistically.
Secondly, RLMPSO is integrated with the Fuzzy Support Vector Machine (FSVM)
to formulate an efficient RLMPSO-FSVM model. Specifically, RLMPSO-FSVM
comprises an ensemble of linear FSVM classifiers that are constructed using
RLMPSO to perform parameter tuning, feature selection, as well as training sample
selection. To evaluate the performance of the proposed RLMPSO-FSVM model, a
benchmark image database is employed. Thirdly, to further improve efficiency, a
two-stage RLMPSO-FSVM model is devised. It consists of a global recognition
stage and a local verification stage. In addition, enhancement of the RLMPSO model
is introduced by incorporating additional search operations. The enhanced RLMPSO
model (i.e. ERLMPSO) comprises three layers, namely, a global layer with four search operations, a local layer with one search operation, and a component-based
layer with twelve search operations. Finally, the proposed two-stage ERLMPSOFSVM
model is applied to a real-world Malaysian vehicle license plate recognition
(VLPR) task. A high recognition rate of 98.1% has been achieved, confirming the
effectiveness of the proposed two-stage ERLMPSO-FSVM model in tackling the
license plate recognition problem.