Content Based Image Retrieval System

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CBIR: Content Based Image Retrieval

Rami Al-Tayeche (237262)
Ahmed Khalil (296918)

Supervisor: Professor Aysegul Cuhadar

A report submitted in partial fulfillment of the requirements of 94.498 Engineering Project

Department of Systems and Computer Engineering
Faculty of Engineering
Carleton University

April 4, 2003


The purpose of this report is to describe our research and solution to the problem of designing a Content Based Image Retrieval, CBIR system. It outlines the problem, the proposed solution, the final solution and the accomplishments achieved. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Firstly, this report outlines a description of the primitive features of an image; texture, colour, and shape. These features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features, are then explained. Our final result was a MatLab built software application, with an image database, that utilized texture and colour features of the images in the database as the basis of comparison and retrieval. The structure of the final software application is illustrated. Furthermore, the results of its performance are illustrated by a detailed example.


We would like to thank our supervisor Professor Aysegul Cuhadar for her continuous feedback and support throughout the year that helped us throughout our project. We would also like to thank Professor Richard Dansereau, Assistant Professor at the Department of Systems and Computer Engineering, for his feedback. Furthermore, we acknowledge the support and feedback of our colleagues and friends, Osama Adassi, Nadia Khater, Aziz El-Solh, and Mike Beddaoui.

Table of Contents

1. Introduction to CBIR1

1.1 Definition1
1.2 Applications of CBIR2
1.3 CBIR Systems2

2. Problem Motivation3

3. Problem Statement4

4. Proposed Solution4

5. Accomplishments5

6. Overview of Report5

7. Background6

7.1 Colour6
7.1.1 Definition6
7.1.2 Methods of Representation7
7.2. Texture10
7.2.1 Definition10
7.2.2 Methods of Representation11 Co-occurrence Matrix12 Tamura Texture14 Wavelet Transform15
7.3 Shape18
7.3.1 Definition18
7.3.2 Methods of Representation19

8. Project Details20

8.1 Colour20
8.1.1 Quadratic Distance Metric20
8.1.2 Histograms20
8.1.3 Similarity Matrix21
8.1.4 Results25
8.2 Texture29
8.2.1 Pyramid-Structured Wavelet Transform29
8.2.2 Energy Level30
8.2.3 Euclidean Distance31
8.3 GUI31
8.4 Database34
8.5 Example34
8.5.1 Colour Extraction & Matching35
8.5.2 Texture Extraction & Matching37

9. Conclusions38

10. References40

11. Appendices43

Appendix - A: Recommended Readings43
CBIR Systems43
Haar Wavelet43
Daubechies Wavelet43
Appendix - B: MatLab Code45

List Of Figures

Figure: Sample Image and its Corresponding Histogram…7
Figure: Image example13
Figure: Classical Co-occurrence matrix13
Figure: Haar Wavelet Example…16
Figure: Daubechies Wavelet Example17
Figure: Boundary-based & Region-based… [16]18
Figure: Colour Histograms of two images.22
Figure: Minkowski Distance Approach…24
Figure: Quadratic Distance Approach…24
Figure: Similarity Matrix A, with a diagonal of ones…[3]25 Figure: Tested Images…26
Figure: Pyramid-Structured Wavelet Transform.30
Figure: GUI Design, aDemo.fig…32
Figure: Menu Editor specification for the menu…33
Figure: Application window at runtime…33
Figure: Image Database…34
Figure: The query...
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