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9781852333812

Principles of Visual Information Retrieval

by
  • ISBN13:

    9781852333812

  • ISBN10:

    1852333812

  • Format: Hardcover
  • Copyright: 2001-02-01
  • Publisher: Springer Verlag

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Supplemental Materials

What is included with this book?

Summary

Principles of Visual Information Retrieval introduces the basic concepts and techniques in VIR and develops a foundation that can be used for further research and study.Divided into 2 parts, the first part describes the fundamental principles. A chapter is devoted to each of the main features of VIR, such as colour, texture and shape-based search. There is coverage of search techniques for time-based image sequences or videos, and an overview of how to combine all the basic features described and integrate context into the search process.The second part looks at advanced topics such as multimedia query, specification, visual learning and semantics, and offers state-of-the-art coverage that is not available in any other book on the market.This book will be essential reading for researchers in VIR, and for final year undergraduate and postgraduate students on courses such as Multimedia Information Retrieval, Multimedia Databases, Computer Vision and Pattern Recognition.

Table of Contents

List of Contributors
xvii
PART I: Fundamental Principles 1(196)
Visual Information Retrieval: Paradigms, Applications, and Research Issues
3(8)
Michael S. Lew
Thomas S. Huang
Introduction
3(1)
Retrieval Paradigms
3(2)
Applications
5(2)
Architecture, Real Estate, and Interior Design
6(1)
Biochemical
6(1)
Digital Catalog Shopping
6(1)
Education
7(1)
Film and Video Archives
7(1)
Medicine
7(1)
Research Issues
7(2)
Summary
9(2)
References
9(2)
Color-Based Retrieval
11(40)
Theo Gevers
Introduction
11(2)
Color Fundamentals
13(2)
Color Appearance
15(9)
The Light Source
16(2)
The Object
18(1)
The Observer
19(5)
Colorimetry
24(8)
XYZ System
24(3)
RGB System
27(1)
HSI System
28(1)
YIQ and YUV Systems
29(1)
Color Order Systems
30(2)
Color Invariance
32(7)
Reflection from Inhomogeneous Dielectric Materials
33(1)
Reflectance with White Illumination
34(3)
Color Constancy
37(2)
Color System Taxonomy
39(8)
Gray-Value System
39(1)
RGB Color System
40(1)
rgb Color System
40(1)
XYZ Color System
41(1)
xyz Color System
42(1)
U*V*W* Color System
42(1)
L*a*b* Color System
43(1)
I1I2I3 Color System
44(1)
YIQ and YUV Color Systems
45(1)
HSI Color System
45(1)
Color Ratios
46(1)
Color and Image Search Engines
47(1)
Conclusion
47(4)
References
48(3)
Texture Features for Content-Based Retrieval
51(36)
Nicu Sebe
Michael S. Lew
Introduction
51(4)
Human Perception of Texture
52(2)
Approaches for Analyzing Textures
54(1)
Texture Models
55(21)
Parametric PDF Methods
57(5)
Non-Parametric PDF Methods
62(4)
Harmonic Methods
66(2)
Primitive Methods
68(4)
Fractal Methods
72(2)
Line Methods
74(1)
Structural Methods
75(1)
Texture in Content-Based Retrieval
76(5)
Texture Segmentation
77(2)
Texture Classification and Indexing
79(1)
Texture for Annotation
80(1)
Summary
81(6)
References
82(5)
State of the Art in Shape Matching
87(34)
Remco C. Veltkamp
Michiel Hagedoorn
Introduction
87(2)
Approaches
89(8)
Global Image Transforms
90(1)
Global Object Methods
90(3)
Voting Schemes
93(1)
Computational Geometry
94(3)
Finite Point Sets
97(3)
Bottleneck Matching
98(1)
Minimum Weight Matching
99(1)
Uniform Matching
99(1)
Minimum Deviation Matching
99(1)
Hausdorff Distance
99(1)
Transformation Space Subdivision
100(1)
Curves
100(7)
Turning Function
102(1)
Signature Function
103(1)
Affine Arclength
104(1)
Reflection Metric
104(1)
Hausdorff Distance
105(1)
Frechet Distance
106(1)
Size Function
107(1)
Pixel Chains
107(1)
Regions
107(5)
Turning Function
107(1)
Frechet Distance
108(1)
Hausdorff Distance
109(2)
Area of Overlap and Symmetric Difference
111(1)
Robustness
112(2)
Software
114(7)
References
115(6)
Feature Similarity
121(24)
Jean-Michel Jolion
Introduction
121(1)
From Images to Similarity
121(4)
Pre-attentive vs. Attentive Features and Similarities
121(1)
Distance vs. Similarity
122(3)
From Features to Similarity
125(5)
Complete vs. Partial Feature
125(1)
Global vs. Local Feature
126(1)
About Histograms
127(2)
Global vs. Individual Comparison of Features
129(1)
Similarity Between Two Sets of Points
130(3)
Multidimensional Voting
130(1)
Graph-Based Matching
131(2)
Similarity Between Histograms
133(4)
Classic Distances
133(2)
The Unfolded Distance
135(1)
The Earth Mover's Distance
135(2)
Merging Similarities
137(2)
An Information Retrieval Approach
137(1)
A Probabilistic Model
138(1)
Similarity Improvement
139(2)
Conclusions
141(4)
References
141(4)
Feature Selection and Visual Learning
145(18)
Michael S. Lew
Introduction
145(1)
Simple Semantics
145(1)
Complex Backgrounds
146(1)
Feature Selection
147(8)
Class Separability
148(1)
Probabilistic Separation
148(1)
Mahalanobis Distance
149(1)
Optimal Search
150(1)
Sequential Forward Selection (SFS)
151(1)
Generalized Sequential Forward Selection (GSFS)
151(1)
Sequential Backward Selection (SBS)
152(1)
Generalized Sequential Backward Selection (GSBS)
152(1)
Plus L Take Away R Selection (LRS)
152(1)
Floating Methods
152(1)
Stochastic Methods
153(1)
Neural Network Methods
154(1)
Direct Methods
154(1)
Summary of the Feature Selection Algorithms
155(1)
Case Studies on Performance of Feature Selection Methods
155(1)
Content-Based Retrieval
156(3)
Discussion
159(1)
Summary
160(3)
References
161(2)
Video Indexing and Understanding
163(34)
Michael S. Lew
Nicu Sebe
Paul C. Gardner
Introduction
163(5)
Video Query Formulation
164(1)
Video Categorization
164(1)
Searching
165(2)
Browsing
167(1)
Viewing
168(1)
Video Analysis and Processing
168(24)
Video Shots
168(21)
Video Stories
189(1)
Video Parsing
190(1)
Video Summaries
191(1)
Automatic Attributes Generation for Video
191(1)
Discussion
192(5)
References
193(4)
PART II: Advanced Topics 197(154)
Query Languages for Multimedia Search
199(20)
Shi-Kuo Chang
Erland Jungert
Introduction
199(2)
Query Languages for Multimedia Databases
201(1)
Query Languages for Heterogeneous Multimedia Databases
202(1)
The σ-Query Language
203(12)
basic Concepts
204(2)
A General σ-Operator for σ-Queries
206(2)
The ΣQL Query Language
208(1)
Querying for Multisensor Data Fusion
209(6)
Discussion
215(4)
References
215(4)
Relevance Feedback Techniques in Image Retrieval
219(40)
Yong Rui
Thomas S. Huang
Introduction
219(3)
The Retrieval Process Based on Relevance Feedback
222(2)
Parameter Updating: a Heuristic Approach
224(3)
Update of the Query Vector qi
224(1)
Update of wi
225(1)
Update of u
226(1)
Parameter Updating: Optimal Approaches
227(4)
Parameter Update at the Representation Level
227(3)
Practical Considerations
230(1)
Parameter Update at Both Levels: Optimal Approaches
231(12)
Quadratic in both φ() and ψi()
231(6)
Linear in φ() and Quadratic in ψi()
237(6)
Case Studies
243(11)
Data Sets
243(1)
Experiments for Algorithms in Sections 9.3.1 and 9.3.2
244(1)
Experiments for Algorithm in Section 9.3.3
245(8)
Experiments for Algorithms in Section 9.5.2
253(1)
Conclusions
254(2)
Acknowledgment
256(3)
References
257(2)
Mix and Match Features in the ImageRover Search Engine
259(20)
Stan Sclaroff
Marco La Cascia
Saratendu Sethi
Leonid Taycher
Introduction
259(2)
Relevance Feedback: Background
261(1)
Unified Feature Representation
262(6)
Textual Statistics
262(2)
Visual Statistics
264(3)
Combined Vector Space
267(1)
Dimensionality Reduction
267(1)
Relevance Feedback
268(3)
Mixing Features and Distance Metrics
269(1)
Relevance Feedback Algorithm
270(1)
System Implementation
271(2)
User Interface
271(1)
Example Search
271(2)
Summary
273(6)
References
275(4)
Integrating Analysis of Context and Image Content
279(18)
Vassilis Athitsos
Charles Frankel
Michael J. Swain
Introduction
279(1)
Extracting Relevant Text
279(3)
Relevant Text in HTML Documents
280(2)
Other Document Formats
282(1)
Image Classification: Photographs vs. Graphics
282(12)
What are Photographs and Graphics?
283(1)
Differences Between Photographs and Graphics
283(1)
Image Metrics
284(4)
Constructing the Decision Trees
288(1)
Preparation of Training and Testing Sets
288(1)
Reasons for Using Multiple Decision Trees
289(1)
Results
290(1)
Improved Learning Techniques
291(1)
Image Context Metrics
291(1)
Mixed Images
292(1)
Other Approaches
293(1)
Other Types of Image Classification
294(1)
Detecting Portraits
294(1)
Distinguishing Color from Gray-Scale Images
294(1)
Filtering Pornographic Images
294(1)
User Interface Issues in Images Search
295(2)
References
295(2)
Semantic-Based Retrieval of Visual Data
297(22)
Clement Leung
Simon So
Audrey Tam
Dwi Sutanto
Philip Tse
Introduction
297(1)
Two Kinds of Visual Contents
297(2)
Primitive Contents
297(1)
Complex Contents
298(1)
Levels of Complex Contents
298(1)
Multiple Search Space Pruning
299(4)
Using Feature Vectors for Primitive Content Search
303(3)
Inverted Image Indexing Using Composite Bitplane Signature
306(3)
Using Semantic Data Models for Complex Content Search
309(4)
The Basic Ternary Fact Model
310(1)
Natural Language Indexing
311(1)
Logical and Physical Index Structure
312(1)
Discussion and Future Work
313(6)
References
316(3)
Trademark Image Retrieval
319(32)
John P. Eakins
Introduction
319(1)
Trademark Search and Registration
320(5)
General Principles
320(1)
Searching for Word Marks
320(1)
Searching for Device (Image) Marks
321(2)
Other Applications of Trademark Image Matching
323(1)
System Requirements for Trademark Image Retrieval
323(2)
Techniques for Image Retrieval
325(8)
The Growth of CBIR
325(1)
Techniques for Shape Retrieval
325(4)
Other Types of Image Retrieval Techniques
329(1)
Retrieval Efficiency
330(1)
Effectiveness of CBIR Techniques
331(2)
Trademark Image Retrieval Systems
333(11)
System Design Issues
333(1)
Manually Based Techniques for Similarity Retrieval
334(1)
CBIR Techniques for Similarity Retrieval
334(9)
Logo Recognition
343(1)
Conclusions
344(7)
Effectiveness of Current Techniques
344(1)
Future Prospects
345(1)
References
346(5)
Author Index 351(2)
Subject Index 353

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