Optimized Two-Stage Ensemble Model for License Plate Recognition | Memetically Optimized Two-Stage Fuzzy Support Vector Machine Ensemble Model for License Plate Recognition

Hussein Samma (u. a.)

ISBN 10: 6139914353 / ISBN 13: 9786139914357

Published by LAP LAMBERT Academic Publishing, 2018

About this Item

Optimized Two-Stage Ensemble Model for License Plate Recognition | Memetically Optimized Two-Stage Fuzzy Support Vector Machine Ensemble Model for License Plate Recognition | Hussein Samma (u. a.) | Taschenbuch | 132 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139914357 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 114860163

Bibliographic Details

• Title: Optimized Two-Stage Ensemble Model for License Plate Recognition | Memetically Optimized Two-Stage Fuzzy Support Vector Machine Ensemble Model for License Plate Recognition
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 6139914353
• ISBN 13: 9786139914357
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 215 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.