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IMAGE SLICING AND STATISTICAL LAYER APPROACHES FOR
CONTENT-BASED IMAGE RETRIEVAL
By
JEHAD QUBIEL ODEH AL-NIHOUD
Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in
Fulfilment of the Requirements for the Degree of Doctor of Philosophy
December 2004
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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfilment of the
requirement for the degree of Doctor of Philosophy
IMAGE SLICING AND STATISTICAL LAYER APPROACHES FOR
CONTENT-BASED IMAGE RETRIEVAL
By
JEHAD QUBIEL ODEH AL-NIHOUD
December 2004
Chairman: Associate Professor Hajah Fatimah Ahmad, Ph.D.
Faculty: Computer Science and Information Technology
Two new approaches for colour features representation and comparison in digital images
to handle various problems in the field of content-based image retrieval are proposed.
The first approach is a double-layered system utilising a new technique, which is based
on image slicing, combined with statistical features extracted and compared in each layer
(ISSL). The images database is filtered in the first layer based on the similarities of
brightness compared with the query image and ranked in the second layer, based on the
similarities of the contrast values between the query image and the set of candidate
images retrieved through the first layer. Although different distance measurements are
available, the city block known as L1-norm distance measurement is used. This is due to
its speed efficiency and accuracy. Different experiments are applied to different database
sets, containing different number of images. The results show that the approach is
scalable to the varying size of the database, robust, accurate, and fast. A comparison
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between the colour histogram approach and the proposed approach shows that the
proposed system is more accurate and the speed of performance is much better. A new
paradigm to choose the proper threshold value is proposed based on the autocorrelation
of the distance vector. Moreover, an image retrieval system based on entropy as a visual
discriminator is developed and compared with ISSL. The results show that the proposed
ISSL approach is able to achieve better precision and reaches higher recall levels as
compared with entropy approach.
The second proposed technique for colour based retrieval is the Eigenvalues approach.
Findings show that the interpretation of the Eigenvalues, as identity or signature for the
square matrix, makes it possible to map this concept to the different bands of the image.
The approach relies on calculating the accumulative distances between the query image
and the images database, using the accumulative Eigenvalues of each band. The approach
is tested, using different image queries over different database sets and the results are
promising. Furthermore, the proposed approach is compared with ISSL approach and
entropy approach, using different query images over a database set of 2000 images.
In addition, a shape-based retrieval system is proposed. The system is double-layered, in
which the first layer is used to filter the images database based on colour similarity. This
allows the reduction in the number of candidate images, which need to be manipulated,
using the shape retrieval technique in the second layer. The technique utilises a low-level
image processing operations with “Dilate” as a morphological operator. Laplacian of
Gaussian (LoG) is used to smoothen and detect the edges of the objects. Dilate on the
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other hand is used to solidify the object and fill in the holes, and correlation coefficient is
proposed as a new means to shape similarity measurement. Experiments show that the
approach is fast, flexible, and the retrieval of images is highly accurate. It is also able to
overcome the numerous problems that are associated with the usage of the low-level
image processing operation in image retrieval.
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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai
memenuhi keperluan untuk ijazah Doktor Falsafah
PENDEKATAN PENGHIRISAN IMEJ DAN LAPISAN BERSTATISTIK UNTUK
DAPATAN SEMULA IMEJ BERASASKAN KANDUNGAN
Oleh
JEHAD QUBIEL ODEH AL-NIHOUD
Disember 2004
Pengerusi: Profesor Madya Hajah Fatimah Ahmad, Ph.D.
Fakulti: Sains Komputer dan Teknologi Maklumat
Hasil kajian ini mencadangkan dua pendekatan untuk perwakilan dan perbandingan ciri-
ciri warna di dalam imej digital bagi menangani pelbagai masalah di dalam bidang
dapatan semula imej berasas kandungan. Pendekatan pertama ialah penggunaan teknik
baru iaitu sistem lapisan berganda. Sistem ini berasaskan penghirisan imej iaitu
penggunaan gabungan ciri-ciri statistik dan perbandingan di dalam setiap lapisan (ISSL).
Ianya dilakukan dengan menapis imej-imej dalam pangkalan data dalam lapisan pertama
berdasarkan kepada kesamaan kecerahan sesuatu imej dan dibandingkan dengan imej
carian dan dikelaskan di dalam lapisan kedua. Pengkelasan ini adalah berdasarkan kepada
kesamaan dalam nilai keterangan bagi sesuatu kecerahan imej di antara imej carian dan
set imej-imej capaian terpilih melalui lapisan pertama.
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Pendekatan ini juga menggunakan teknik pengukuran L1-norm sebagai pengukuran jarak
disebabkan oleh kecekapan dan ketepatan kelajuannya dalam membuat penapisan dan
perbandingan imej-imej. Pelbagai kajian dijalankan ke atas set-set pangkalan data yang
berbeza yang mengandungi bilangan imej yang berlainan. Hasil kajian ini menunjukkan
bahawa pendekatan ini bersesuaian digunakan untuk pelbagai saiz pangkalan data dimana
ianya, teguh, tepat dan pantas dalam menghasilkan dapatan semula imej. Perbandingan
juga dibuat di antara penggunaan pendekatan histogram warna dan pendekatan yang
dicadangkan dan ianya menunjukkan bahawa sistem yang dicadangkan lebih tepat dan
prestasi kelajuannya adalah lebih baik. Paradigma baru juga digunakan untuk memilih
nilai ambang yang dicadangkan berdasarkan kepada autokorelasi bagi jarak vektor.
Sistem berasaskan dapatan semula imej ke atas entropi sebagai visual diskriminator juga
dibangun dan dibandingkan dengan ISSL dan hasil daripada ini ianya menunjukkan
bahawa pendekatan ISSL yang dicadangkan boleh digunakan untuk mencapai ketepatan
yang lebih baik dan mencapai tahap perolehan yang lebih tinggi berbanding dengan
pendekatan entropi.
Teknik kedua yang dicadangkan untuk dapatan semula berasas warna adalah berdasarkan
pendekatan Eigenvalues. Hasil kajian menunjukkan interprestasi Eigenvalues sebagai
identiti atau pengenalan untuk matriks segiempat sama bagi membolehkan ianya memeta
konsep ini ke atas jalur-jalur imej yang berbeza. Pendekatan ini bergantung kepada
perkiraan jarak terkumpul di antara imej carian dan imej-imej di dalam pangkalan data
menggunakan Eigenvalues yang terkumpul bagi setiap jalur. Pendekatan ini diuji dengan
menggunakan carian imej yang berbeza ke atas set-set pangkalan data yang berlainan dan
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hasilnya menunjukkan ianya adalah lebih tepat. Pendekatan ini juga dibandingkan dengan
pendekatan ISSL dan pendekatan entropi dengan melakukan ujian yang menggunakan
imej carian yang berbeza di dalam set pangkalan data yang mempunyai 2000 imej.
Kajian ini juga mencadangkan sistem dapatan semula berasas bentuk. Sistem ini
mempunyai dua lapisan di mana lapisan pertama digunakan untuk menapis imej-imej
dalam pangkalan data berdasarkan kepada kesamaan warna pada imej tersebut. Ini akan
menyebabkan penurunan dalam bilangan imej terpilih yang perlu dimanipulasi dengan
menggunakan teknik dapatan semula bentuk di dalam lapisan kedua. Teknik ini
menggunakan operasi pemprosesan imej bertahap rendah dengan ‘Dilate’ sebagai
operator morfologi. Laplacian of Gaussian (LoG) pula digunakan untuk melicinkan dan
mengesan sempadan-sempadan bagi sesuatu objek manakala ‘Dilate’ digunakan untuk
memadatkan dan mengisi objek-objek ini ke dalam lubang-lubang dan pekali korelasi
digunakan sebagai satu kaedah untuk mengukur kesamaan bentuk. Kajian menunjukkan
pendekatan ini lebih pantas, fleksibel dan dapatan semula imejnya lebih tepat. Ianya juga
boleh menyelesaikan pelbagai masalah yang melibatkan penggunaan operasi
pemprosesan imej bertahap rendah di dalam bidang dapatan semula imej.
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ACKNOWLEDGEMENT
In the name of ALLAH, The Most Gracious, and Most Merciful
This thesis would not have been possible without the help of many who have contributed
one way or another in its creation. While it is difficult to acknowledge them all, I would
like to express my thanks and appreciation to whomever contributes to the success of this
research work.
However, I would like to convey my sincere thanks and deepest appreciation to my
supervisory committee, Assoc. Prof. Hajah Fatimah Ahmad, Assoc. Prof. Mohamed
Othman, and Dr. Rozita Johari for their help, guidance, patience and encouragement
rendered throughout the different phases of this research.
I would also like to convey my thanks to the Faculty of Computer Science and
Information Technology, the Sultan Abdul Samad Library and Universiti Putra Malaysia
for assisting me in my study.
I express my warmest gratitude to my mother and the soul of my late father, who never
let me believe I could not succeed in this work or in any other challenges in my life. Also,
special thanks to my wife, brothers, and sisters, for their prayers, love and encouragement
during my study. Finally, I am grateful to all of my friends, Atheer Abas, Mueen Ahmad,
Belal Yousf, Nbhan Salih, and Hakim Mellah for their support.
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I certify that an Examination Committee met on 2nd
December 2004 to conduct the final
examination of Jehad Qubiel Odeh, on his Doctor of Philosophy thesis entitled “Image
Slicing and Statistical Layer Approaches for Content-Based Image Retrieval” in
accordance with Universiti Pertanian Malaysia (Higher Degree) Act 1980 and Universiti
Pertanian Malaysia (Higher Degree) Regulations 1981. The Committee recommends that
the candidate be awarded the relevant degree. Members of the Examination Committee
are as follows:
MOHD HASAN SELAMAT, M.Phil.
Associate Professor
Faculty of Computer Science and Information Technology
University Putra Malaysia
(Chairman)
RAMLAN MAHMOD, Ph.D.
Associate Professor
Faculty of Computer Science and Information Technology
University Putra Malaysia
(Member)
RAHMITA WIRZA, Ph.D.
Faculty of Computer Science and Information Technology
University Putra Malaysia
(Member)
TENGKU MOHD TENGKU SEMBOK, Ph.D.
Professor
Faculty of Information Science and Technology
University Kebangsaan Malaysia
(Independent Examiner)
___________________________________
ZAKARIAH ABD RASHID, Ph.D.
Professor/Deputy Dean
School of Graduate Studies
Universiti Putra Malaysia
Date:
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This Thesis submitted to the Senate of Universiti Putra Malaysia and has been accepted
as fulfilment of the requirement for the degree of Doctor of Philosophy. The members of
the Supervisory Committee are as follows:
HAJAH FATIMAH AHMAD, Ph.D.
Associate Professor
Faculty of Computer Science and Information Technology
Universiti Putra Malaysia
(Chairman)
MOHAMAD OTHMAN, Ph.D.
Associate Professor
Faculty of Computer Science and Information Technology
Universiti Putra Malaysia
(Member)
ROZITA JOHARI, Ph.D.
Faculty of Computer Science and Information Technology
Universiti Putra Malaysia
(Member)
_____________________________
AINI IDERIS, Ph.D.
Professor/Dean
School of Graduate School
Universiti Putra Malaysia
Date:
xii
DECLARATION
I hereby declare that the thesis is based on my original work except for quotations and
citations, which have been duly acknowledged. I also declare that it has not been
previously or concurrently submitted for any other degree at UPM or other institutions.
________________________________
JEHAD QUBIEL ODEH ALNIHOUD
Date:
xiii
TABLE OF CONTENTS
Page
ABSTRACT iii
ABSTRAK vi
ACKNOWLEDGEMENTS ix
APPROVAL x
DECLARATION xii
LIST OF TABLES xvii
LIST OF FIGURES xix
LIST OF ABBREVIATIONS xxv
CHAPTER
1 INTRODUCTION 1
1.1 Problems Statement 3
1.2 Proposed Approaches 6
1.3 Research Objectives 7
1.4 Scope of the Research 8
1.5 Main Contributions 8
1.6 Thesis Structure 9
2 LITERATURE REVIEW 12
2.1 Introduction 12
2.2 Content-Based Image Retrieval (CBIR) 12
2.3 Colour-Based Retrieval 17
2.3.1 Colour Histogram Approach 17
2.3.2 Colour Pair Segmentation 20
2.3.3 Entropy as a Visual Feature for Colour-Based Retrieval 22
2.4 Shape-Based Retrieval 23
2.4.1 Global Shape Feature-Based Methods 24
2.4.2 Region-Based Querying Methods 25
2.4.3 Region Graph-Based Image Retrieval 26
2.4.4 Model-Based Querying 28
2.5 Commercial Systems 29
2.6 Interfacing 30
2.7 Summary 32
3 METHODOLOGY AND SYSTEM DESIGN 34
3.1 Introduction
3.2 Prototyping
3.2.1 Evolutionary Development Process
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3.2.2 Pilot System
3.3 Proposed Framework and Methodology
3.4 Similarity Measurements and Evaluation Techniques
3.4.1 Similarity Measurements
3.4.2 Evaluation Techniques
3.4.3 Threshold
3.4.4 Data
3.5 Summary
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4 COLOUR RETRIEVAL MODEL BASED ON IMAGE SLICING
AND STATISTICAL LAYERS (ISSL)
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4.1 Introduction 49
4.2 System Design and Implementation 49
4.2.1 The Interface 49
4.2.2 System Architecture 50
4.3 Image Slicing Technique to Preserve the Spatial Similarity 51
4.4 Definitions and Algorithms 53
4.5 Colour Spaces Comparison using ISSL 57
4.6 ISSL Evaluation 65
4.6.1 Experimental Setup 65
4.6.2 Average Recall versus Average Precision Testing 66
4.7 Colour Histogram Approach 86
4.8 ISSL versus Colour Histogram Approach 87
4.8.1 Experimental Setup 87
4.8.2 Recall and Precision of ISSL versus Colour Histogram 89
4.8.3 Ranking Accuracy of ISSL versus Colour Histogram 92
4.8.4 City Block versus Euclidean Distance Measures using
Colour Histogram
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4.8.5 Run-Time Performance of ISSL versus Colour Histogram 101
4.9 Discussion 102
4.10 Summary 103
5 ISSL VERSUS ENTROPY AS A VISUAL DISCRIMINATOR
FOR COLOUR-BASED IMAGE RETRIEVAL
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5.1 Introduction
5.2 Entropy Approach Overview
5.3 Definition and Algorithms
5.4 Experimental Setup
5.5 Experiments
5.5.1 Average Precision of Entropy-Based Similarity (Database Set
200 Images)
5.5.2 Average Precision of Entropy-Based Similarity (Database
Set 600 Images)
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5.5.3 Average Precision of Entropy-Based Similarity (Database
Set 1000 Images)
5.5.4 Entropy versus ISSL (Database Set 2000 Images)
5.5.5 Run-Time Performance of Entropy versus ISSL
5.6 Discussion
5.7 Summary
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6 EIGENVALUES AS A STATISTICAL FEATURE
DISCRIMINATOR FOR COLOUR-BASED IMAGE
RETRIEVAL
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6.1 Introduction
6.2 Eigenvalues Approach Overview
6.3 Eigenvalues Definition and Algorithms
6.4 Experimental Setup
6.5 Experiments
6.5.1 Average Precision of Eigenvalues-Based Similarity
(Database Set 200 Images)
6.5.2 Average Precision of Eigenvalues-Based Similarity
(Database Set 600 Images)
6.5.3 Average Precision of Eigenvalues-Based Similarity
(Database Set 1000 Images)
6.5.4 Eigenvalues versus ISSL and Entropy (Database Set 2000
Images)
6.5.5 Run-Time Performance of Eigenvalues-Based Similarity
6.6 Discussion
6.7 Summary
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7 INTEGRATION OF ISSL APPROACH FOR COLOUR
RETRIEVAL AND SHAPE APPROACH BASED ON
LOW LEVEL IMAGE PROCESSING OPERATIONS
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7.1 Introduction
7.2 System Design
7.2.1 System Architecture
7.2.2 The Interface
7.3 Neighbourhood Operations
7.4 Mar/Hildreth Criteria for Edge Operators
7.5 Edge Detection Operators
7.5.1 Laplacian
7.5.2 Laplacian of Gaussian
7.6 Morphology
7.6.1 Structuring Elements
7.6.2 Dilate
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7.7 Matching by Correlation Coefficient
7.8 Proposed Shape Retrieval Algorithms (SRAs)
7.9 Experimental Setup
7.10 Experiments
7.10.1 Retrieve Similar Images
7.10.2 Retrieve Less Images
7.10.3 Retrieve More Images
7.10.4 Experimental Results
7.10.5 Run-Time Performance
7.11 Discussion
7.12 Summary
8 CONCLUSION AND FUTURE WORK 8.1 Results Summary
8.2 Future Studies
8.2.1 Future Directions to Improve the Colour-Based Approaches
8.2.2 Future Directions to Improve the Shape-Based Approach
REFERENCES
APPENDICES
BIODATA OF THE AUTHOR
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LIST OF TABLES
Table
Page
4.1 Recall and Precision (ISSL, Database Set 200 Images)
4.2 Recall and Precision (ISSL, Database Set 400 Images)
4.3 Recall and Precision (ISSL, Database Set 600 Images)
4.4 Recall and Precision (ISSL, Database Set 800 Images)
4.5 Recall and Precision (ISSL, Database Set 1000 Images)
4.6 Recall and Precision (ISSL, Database Set 1200 Images)
4.7 Recall and Precision (ISSL, Database Set 1400 Images)
4.8 Recall and Precision (ISSL, Database Set 1600 Images)
4.9 Recall and Precision (ISSL, Database Set 1800 Images)
4.10 Recall and Precision (ISSL, Database Set 2000 Images)
4.11 Overall Recall and Precision of ISSL
4.12 Number of Relevant Images to Queries (ISSL versus Colour
Histogram)
4.13 The Recall & Precision of ISSL versus Colour Histogram (Database
Set 2000 Images)
4.14 Speed of Performance of City Block versus Euclidean (Colour
Histogram Approach)
4.15 Speed of Performance of the ISSL versus Colour Histogram
5.1 Number of Relevant Images to Queries (Entropy, Database Set 200
Images)
5.2 Average Precision (Entropy, Database Set 200 Images)
5.3 Number of Relevant Images to Queries (Entropy, Database Set 600
Images)
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5.4 Average Precision (Entropy, Database Set 600 Images)
5.5 Number of Relevant Images to Queries (Entropy, Database Set 1000
Images)
5.6 Average Precision (Entropy, Database Set 1000 Images)
5.7 Number of Relevant Images to Queries (Database Set 2000 Images)
5.8 Average Precision (Entropy, Database Set 2000 Images)
5.9 Average Precision (ISSL, Database Set 2000 Images)
5.10 Run-Time Testing of Entropy versus ISSL
6.1 Number of Relevant Images to Queries (Eigenvalues, Database Set 200
Images)
6.2 Average Precision (Eigenvalues, Database Set 200 Images)
6.3 Number of Relevant Images to Queries (Eigenvalues, Database Set 600
Images)
6.4 Average Precision (Eigenvalues, Database Set 600 Images)
6.5 Number of Relevant Images to Queries (Eigenvalues, Database Set
1000 Images)
6.6 Average Precision (Eigenvalues, Database Set 1000 Images)
6.7 Average Precision (Eigenvalues, Database Set 2000 Images)
6.8 Run-Time Testing of Eigenvalues versus ISSL and Entropy
7.1 LoG Mask
7.2 Number of Relevant Images to Queries (Shape Approach, Database Set
1000 images)
7.3 Recall versus Average Precision ( ε <= 0.30)
7.4 Recall versus Average Precision ( ε <= 0.10)
7.5 Recall versus Average Precision ( ε <= 0.50)
7.6 Run-Time Testing (Shape Approach)
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LIST OF FIGURES
Figure
Page
3.1 Traditional Image Retrieval Model
3.2 Prototyping in Software Development Process
3.3 Iterative Life Cycle
3.4 Proposed Image-Based Retrieval Framework
3.5 Colour Prototypes Creation Based on Incremental Development
Process and Iterative Enhancement Method
3.6 Pilot System of the Proposed Colour and Shape-Based Image Retrieval
Approaches
4.1 Interface of ISSL
4.2 System Overview of ISSL
4.3 Slicing Image to Four Subareas
4.4 Testing the Effectiveness of Slicing Technique
4.5 ISSL1 Algorithm
4.6 ISSL2 Algorithm
4.7 RGB Colour Space
4.8 HSV Colour Space
4.9 Query Image to Test Colour Spaces using ISSL
4.10 Retrieved Set (RGB Colour Space)
4.11 Retrieved Set (YCbCr Colour Space)
4.12 Retrieved Set (HSV Colour Space)
4.13 Colour Spaces Comparison (Recall versus Precision)
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4.14 Query Images (ISSL, Database Set 200 Images)
4.15 Query Images (ISSL, Database Set 400 Images)
4.16 Query Images (ISSL, Database Set 600 Images)
4.17 Query Images (ISSL, Database Set 800 Images)
4.18 Query Images (ISSL, Database Set 1000 Images)
4.19 Query Images (ISSL, Database Set 1200 Images)
4.20 Query Images (ISSL, Database Set 1400 Images)
4.21 Query Images (ISSL, Database Set 1600 Images)
4.22 Query Images (ISSL, Database Set 1800 Images)
4.23 Query Images (ISSL, Database Set 2000 Images)
4.24 Regression between Database Size and Precision
4.25 Regression between Database Size and Recall
4.26 Sample Retrieved Set (ISSL Approach, Database Set 200 Images)
4.27 Sample Retrieved Set (ISSL Approach, Database Set 400 Images)
4.28 Sample Retrieved Set (ISSL Approach, Database Set 600 Images)
4.29 Sample Retrieved Set (ISSL Approach, Database Set 800 Images)
4.30 Sample Retrieved Set (ISSL Approach, Database Set 1000 Images)
4.31 Sample Retrieved Set (ISSL Approach, Database Set 1200 Images)
4.32 Sample Retrieved Set (ISSL Approach, Database Set 1400 Images)
4.33 Sample Retrieved Set (ISSL Approach, Database Set 1600 Images)
4.34 Sample Retrieved Set (ISSL Approach, Database Set 1800 Images)
4.35 Sample Retrieved Set (ISSL Approach, Database Set 2000 Images)
4.36 Colour Histogram Values Extraction
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4.37 Query Images (ISSL versus Colour Histogram)
4.38 Sample1 of Retrieved Set (Colour Histogram Approach)
4.39 Sample2 of Retrieved Set (Colour Histogram Approach)
4.40 Sample3 of Retrieved Set (Colour Histogram Approach)
4.41 Query Image (Image Index 327)
4.42 Retrieved Set (ISSL)
4.43 Retrieved Set (Colour Histogram)
4.44 Recall versus Precision for ISSL and Colour Histogram
4.45 Query Image (Euclidean versus City Block)
4.46 Retrieved Set using Euclidean Distance Measure
4.47 Retrieved Set using City Block Distance Measure
5.1 System Overview (Entropy)
5.2 Entropy Prototype Interface
5.3 ENDA Algorithm
5.4 ENIQA Algorithm
5.5 Query Images (Entropy, Database Set 200 Images)
5.6 Recall versus Average Precision (Entropy, Database Set 200 Images)
5.7 Sample Retrieved Set (Entropy, Database Set 200 Images)
5.8 Query Images (Entropy, Database Set 600 Images)
5.9 Recall versus Average Precision (Entropy, Database Set 600 Images)
5.10 Sample Retrieved Set (Entropy, Database Set 600 Images)
5.11 Query Images (Entropy, Database Set 1000 Images)
5.12 Recall versus Average Precision (Entropy, Database Set 1000 Images)
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5.13 Sample Retrieved Set (Entropy, Database Set 1000 Images)
5.14 Query Images (Entropy versus ISSL, Database Set 2000 Images)
5.15 Recall versus Average Precision (Entropy versus ISSL)
5.16 Retrieved Set for Query 6 (Entropy, Database Set 2000 Images)
5.17 Retrieved Set for Query 4 (Entropy, Database Set 2000 Images)
6.1 System Overview (Eigenvalues Approach)
6.2 The Interface (Eigenvalues)
6.3 EVFEA1 Algorithm
6.4 EVFEA2 Algorithm
6.5 Query Images (Eigenvalues, Database Set 200 Images)
6.6 Recall versus Average Precision (Eigenvalues, Database Set 200
Images)
6.7 Sample Retrieved Set (Eigenvalues, Database Set 200 Images)
6.8 Query Images (Eigenvalues, Database Set 600 Images)
6.9 Recall versus Average Precision (Eigenvalues, Database Set 600
Images)
6.10 Sample Retrieved Set (Eigenvalues, Database Set 600 Images)
6.11 Query Images (Eigenvalues, Database Set 1000 Images)
6.12 Recall versus Average Precision (Eigenvalues, Database Set 1000
Images)
6.13 Sample Retrieved (Eigenvalues, Database Set 1000 Images)
6.14 Query Images (Eigenvalues, Database Set 2000 Images)
6.15 Recall versus Average Precision (ISSL, Entropy, and Eigenvalues)
6.16 Sample Retrieved Set (Eigenvalues, Database Set 2000 Images)
6.17 Eigenvalues Performance
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7.1 System Overview (Shape Prototype)
7.2 The Interface (Shape Prototype)
7.3 Laplacian Masks (Kernels)
7.4 Dilate Process
7.5 Dilate using Square Structuring Element
7.6 Correlation of f(x, y) and w(x, y) at the Point (s, t)
7.7 SRA1 Algorithm
7.8 SRA2 Algorithm
7.9 Set of Query Images (Shape Approach)
7.10 Result Set of Query 1 ( ε<= 0.30)
7.11 Result Set of Query 2 ( ε<= 0.30)
7.12 Result set of Query 3 ( ε<= 0.30)
7.13 Result Set of Query 4 ( ε<= 0.30)
7.14 Result Set of Query 5 ( ε<= 0.30)
7.15 Result Set of Query 6 ( ε<= 0.30)
7.16 Result Set of Query 7 ( ε<= 0.30)
7.17 Result Set of Query 1 ( ε<= 0.10)
7.18 Result Set of Query 2 ( ε<= 0.10)
7.19 Result Set of Query 3 ( ε<= 0.10)
7.20 Result Set of Query 4 ( ε<= 0.10)
7.21 Result Set of Query 5 ( ε<= 0.10)
7.22 Result Set of Query 6 ( ε<= 0.10)
7.23 Result Set of Query 7 ( ε<= 0.10)
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7.24 Result Set of Query 1 ( ε<= 0.50)
7.25 Result Set of Query 2 ( ε<= 0.50)
7.26 Result Set of Query 3 ( ε<= 0.50)
7.27 Result Set of Query 4 ( ε<= 0.50)
7.28 Result Set of Query 5 ( ε<= 0.50)
7.29 Result Set of Query 6 ( ε<= 0.50)
7.30 Result Set of Query 7 ( ε<= 0.50)
7.31 Recall versus Average Precision (Shape Approach)
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