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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 Learning-based Memetic Particle Swarm Optimization (RLMPSO) model is introduced. Then, RLMPSO is integrated with the Fuzzy Support Vector Machine (FSVM) to formulate an efficient two-stage RLMPSO-FSVM model. Specifically, two-stage 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. Finally, the proposed two-stage RLMPSO-FSVM model is applied to a real-world Malaysian vehicle license plate recognition (VLPR) task.
| Autorius: | Hussein Samma, Junita Mohamad-Saleh, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2018 |
| Knygos puslapių skaičius: | 132 |
| ISBN-10: | 6139914353 |
| ISBN-13: | 9786139914357 |
| Formatas: | 220 x 150 x 8 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Optimized Two-Stage Ensemble Model for License Plate Recognition: Memetically Optimized Two-Stage Fuzzy Support Vector Machine Ensemble Model for License Plate Recognition“
