文档介绍：Particle Swarm Optimization Methods for Pattern
Recognition and Image Processing
by
Mahamed G. H. Omran
Submitted in partial fulfillment of the requirements for the degree Philosophiae
Doctor in the Faculty of Engineering, Built Environment and Information Technology
University of Pretoria
Pretoria
November 2004
Particle Swarm Optimization Methods for Pattern Recognition and Image
Processing
by
Mahamed G. H. Omran
Abstract
Pattern recognition has as its objective to classify objects into different categories and
classes. It is a ponent of artificial intelligence puter vision.
This thesis investigates the application of an efficient optimization method, known as
Particle Swarm Optimization (PSO), to the field of pattern recognition and image
processing. First a clustering method that is based on PSO is proposed. The
application of the proposed clustering algorithm to the problem of unsupervised
classification and segmentation of images is investigated. A new automatic image
generation tool tailored specifically for the verification parison of various
unsupervised image classification algorithms is then developed. A dynamic clustering
algorithm which automatically determines the "optimum" number of clusters and
simultaneously clusters the data set with minimal user interference is then developed.
Finally, PSO-based approaches are proposed to tackle the color image quantization
and spectral unmixing problems. In all the proposed approaches, the influence of PSO
parameters on the performance of the proposed algorithms is evaluated.
Key terms: Clustering, Color Image Quantization, Dynamic Clustering, Image Processing,
Image Segmentation, Optimization Methods, Particle Swarm Optimization, Pattern
Recognition, Spectral Unmixing, Unsupervised Image Classification.
Thesis supervisor: Prof. A. P. Engelbre