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,...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2016
|
| Subjects: | |
| Online Access: | 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/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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.
|
|---|