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Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition

Model pengecaman corak memainkan peranan yang penting dalam banyak aplikasi dunia sebenar seperti pengesanan teks dan pengecaman objek. Pelbagai kaedah termasuk model Kecerdikan Berkomputer (CI) telah dibangunkan untuk menangani masalah pengecaman corak berasaskan imej. Tertumpu kepada model CI,...

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Main Author: Samma, Hussein Salem Ali
Format: Thesis
Language:English
Published: 2016
Subjects:
T Technology
TP155-156 Chemical engineering
Online Access:http://eprints.usm.my/41664/1/Enhanced_Particle_Swarm_Optimization-Based_Models_And_Their_Application_To_License_Plate_Recognition.pdf
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record_format eprints
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TP155-156 Chemical engineering
spellingShingle T Technology
TP155-156 Chemical engineering
Samma, Hussein Salem Ali
Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition
description Model pengecaman corak memainkan peranan yang penting dalam banyak aplikasi dunia sebenar seperti pengesanan teks dan pengecaman objek. Pelbagai kaedah termasuk model Kecerdikan Berkomputer (CI) telah dibangunkan untuk menangani masalah pengecaman corak berasaskan imej. Tertumpu kepada model CI, penyelidikan ini mempersembah model berasaskan pengoptimuman kawanan zarah (PSO) yang cekap serta aplikasinya untuk pengecaman lesen plat. Pertama, model Pengoptimuman Kawanan Zarah Memetik berasaskan pengukuhan pembelajaran yang baharu (RLMPSO) diperkenalkan. Masalah pengoptimuman penanda aras digunakan untuk menilai prestasi RLMPSO, dan kaedah bootstarp digunakan untuk menilai keputusan secara statistik. Kedua, RLMPSO disepadukan dengan mesin Penyokong Vektor Kabur (FSVM) untuk merumuskan model RLMPSO-FSVM yang cekap. Secara khusus, RLMPSO-FSVM terdiri daripada gabungan pengelas linear FSVM yang dibina menggunakan RLMPSO untuk melaksanakan penalaan parameter, pemilihan ciri, serta pemilihan contoh latihan. Untuk menilai prestasi model RLMPSO-FSVM yang dicadangkan, pangkalan data imej penanda aras digunakan. Ketiga, model dua-peringkat RLMPSO-FSVM dicipta untuk mempertingkatkan lagi kecekapan. Ia mengandungi peringkat pengecaman global dan peringkat pengesahan tempatan. Peningkatan model RLMPSO turut diperkenalkan dengan memasukkan operasi carian tambahan. Model RLMPSO yang (ERLMPSO) dipertingkatkan terdiri daripada tiga lapisan, iaitu lapisan global dengan empat operasi carian, lapisan tempatan dengan satu operasi carian, dan lapisan berasaskan komponen dengan dua belas operasi carian. Akhir sekali, model dua-peringkat ERLMPSO-FSVM yang dicadangkan telah digunapakai dalam masalah Pengecaman Plat Lesen Kereta Malaysia (VLPR) yang sebenar. Kadar pengecaman setinggi 98.1% telah diperoleh. Keputusan ini mengesahkan keberkesanan model dua-peringkat ERLMPSO-FSVM yang dicadangkan dalam menangani masalah pengecaman plat lesen. ________________________________________________________________________________________________________________________ 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.
format Thesis
author Samma, Hussein Salem Ali
author_facet Samma, Hussein Salem Ali
author_sort Samma, Hussein Salem Ali
title Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition
title_short Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition
title_full Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition
title_fullStr Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition
title_full_unstemmed Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition
title_sort enhanced particle swarm optimization-based models and their application to license plate recognition
publishDate 2016
url http://eprints.usm.my/41664/1/Enhanced_Particle_Swarm_Optimization-Based_Models_And_Their_Application_To_License_Plate_Recognition.pdf
http://eprints.usm.my/41664/
_version_ 1643710284569772032
spelling my.usm.eprints.41664 http://eprints.usm.my/41664/ Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition Samma, Hussein Salem Ali T Technology TP155-156 Chemical engineering Model pengecaman corak memainkan peranan yang penting dalam banyak aplikasi dunia sebenar seperti pengesanan teks dan pengecaman objek. Pelbagai kaedah termasuk model Kecerdikan Berkomputer (CI) telah dibangunkan untuk menangani masalah pengecaman corak berasaskan imej. Tertumpu kepada model CI, penyelidikan ini mempersembah model berasaskan pengoptimuman kawanan zarah (PSO) yang cekap serta aplikasinya untuk pengecaman lesen plat. Pertama, model Pengoptimuman Kawanan Zarah Memetik berasaskan pengukuhan pembelajaran yang baharu (RLMPSO) diperkenalkan. Masalah pengoptimuman penanda aras digunakan untuk menilai prestasi RLMPSO, dan kaedah bootstarp digunakan untuk menilai keputusan secara statistik. Kedua, RLMPSO disepadukan dengan mesin Penyokong Vektor Kabur (FSVM) untuk merumuskan model RLMPSO-FSVM yang cekap. Secara khusus, RLMPSO-FSVM terdiri daripada gabungan pengelas linear FSVM yang dibina menggunakan RLMPSO untuk melaksanakan penalaan parameter, pemilihan ciri, serta pemilihan contoh latihan. Untuk menilai prestasi model RLMPSO-FSVM yang dicadangkan, pangkalan data imej penanda aras digunakan. Ketiga, model dua-peringkat RLMPSO-FSVM dicipta untuk mempertingkatkan lagi kecekapan. Ia mengandungi peringkat pengecaman global dan peringkat pengesahan tempatan. Peningkatan model RLMPSO turut diperkenalkan dengan memasukkan operasi carian tambahan. Model RLMPSO yang (ERLMPSO) dipertingkatkan terdiri daripada tiga lapisan, iaitu lapisan global dengan empat operasi carian, lapisan tempatan dengan satu operasi carian, dan lapisan berasaskan komponen dengan dua belas operasi carian. Akhir sekali, model dua-peringkat ERLMPSO-FSVM yang dicadangkan telah digunapakai dalam masalah Pengecaman Plat Lesen Kereta Malaysia (VLPR) yang sebenar. Kadar pengecaman setinggi 98.1% telah diperoleh. Keputusan ini mengesahkan keberkesanan model dua-peringkat ERLMPSO-FSVM yang dicadangkan dalam menangani masalah pengecaman plat lesen. ________________________________________________________________________________________________________________________ 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. 2016-02 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/41664/1/Enhanced_Particle_Swarm_Optimization-Based_Models_And_Their_Application_To_License_Plate_Recognition.pdf Samma, Hussein Salem Ali (2016) Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition. PhD thesis, Universiti Sains Malaysia.
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