Particle Swarm Optimization Methods for Pattern Recognition and Image Processing - Mahamed G. H. Omran.pdf - Pattern recognition - musli_com

Particle Swarm Optimization Methods for Pattern

Recognition and Image Processing

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

Mahamed G. H. Omran

Abstract

Pattern recognition has as its objective to classify objects into different categories and

classes. It is a fundamental component of artificial intelligence and computer 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 and comparison 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. Engelbrecht

Thesis co-supervisor: Dr. Ayed Salman

Department of Computer Engineering, Kuwait University, Kuwait

Department of Computer Science

Degree: Philosophiae Doctor

“Obstacles are those frightening things you see when you take your eyes off your

goal.”

Henry Ford

“You will recognize your own path when you come upon it, because you will

suddenly have all the energy and imagination you will ever need.”

Jerry Gillies

iii

Acknowledgments

I address my sincere gratitude to God as whenever I faced any difficulty I used to pray

to God to help me and He always was there protecting and saving me.

Then, I would like to express my warm thanks to Professor Andries

Engelbrecht, who spared no effort in supporting me with all care and patience. I

enjoyed working with him, making every moment I spent in the research work as

enjoyable as can be imagined.

I would like also to thank my co-supervisor Dr. Ayed Salman from Kuwait

University for his continuous guidance, encouragement and patience throughout the

PhD journey. I will never forget the long hours we spent together discussing various

ideas and methods.

Last but not least, I would love to thank my family for their support and care,

especially my mother Aysha and my father Ghassib. May God bless and protect them

both hoping that God will help me to repay them part of what they really deserve. I

also thank my two sisters Ala'a and Esra'a for their help in preparing this thesis.

Contents

Chapter 1

Introduction....................................................................................................................1

1.1 Motivation............................................................................................................1

1.2 Objectives ............................................................................................................2

1.3 Methodology ........................................................................................................3

1.4 Contributions........................................................................................................4

1.5 Thesis Outline ......................................................................................................5

Chapter 2

Optimization and Optimization Methods.......................................................................7

2.1 Optimization ........................................................................................................7

2.2 Traditional Optimization Algorithms ................................................................10

2.3 Stochastic Algorithms ........................................................................................11

2.4 Evolutionary Algorithms ...................................................................................12

2.5 Genetic Algorithms ............................................................................................15

2.5.1 Solution Representation ..............................................................................16

2.5.2 Fitness Function ..........................................................................................16

2.5.3 Selection......................................................................................................17

2.5.4 Crossover ....................................................................................................19

2.5.5 Mutation ......................................................................................................20

2.5.6 The Premature Convergence Problem ........................................................22

2.6 Particle Swarm Optimization .............................................................................23

2.6.1 The PSO Algorithm ....................................................................................23

2.6.2 The lbest Model ..........................................................................................26

2.6.3 PSO Neighborhood topologies ...................................................................28

2.6.4 The Binary PSO ..........................................................................................29

2.6.5 PSO vs. GA .................................................................................................31

2.6.6 PSO and Constrained Optimization ............................................................32

2.6.7 Drawbacks of PSO ......................................................................................33

2.6.8 Improvements to PSO .................................................................................34

2.7 Ant Systems .......................................................................................................45

2.8 Conclusions........................................................................................................46

Chapter 3

Problem Definiton........................................................................................................47

3.1 The Clustering Problem .....................................................................................47

3.1.1 Definitions...................................................................................................48

3.1.2 Similarity Measures ....................................................................................49

3.1.3 Clustering Techniques ................................................................................51

3.1.4 Clustering Validation Techniques...............................................................64

3.1.5 Determining the Number of Clusters ..........................................................69

3.1.6 Clustering using Self-Organizing Maps......................................................75

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