Content-Based Video Retrieval: A Database Perspective

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Springer Science & Business Media, Oct 31, 2003 - Computers - 151 pages
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The area of content-based video retrieval is a very hot area both for research and for commercial applications. In order to design effective video databases for applications such as digital libraries, video production, and a variety of Internet applications, there is a great need to develop effective techniques for content-based video retrieval. One of the main issues in this area of research is how to bridge the semantic gap between low-Ievel features extracted from a video (such as color, texture, shape, motion, and others) and semantics that describe video concept on a higher level. In this book, Dr. Milan Petkovi6 and Prof. Dr. Willem Jonker have addressed this issue by developing and describing several innovative techniques to bridge the semantic gap. The main contribution of their research, which is the core of the book, is the development of three techniques for bridging the semantic gap: (1) a technique that uses the spatio-temporal extension of the Cobra framework, (2) a technique based on hidden Markov models, and (3) a technique based on Bayesian belief networks. To evaluate performance of these techniques, the authors have conducted a number of experiments using real video data. The book also discusses domains solutions versus general solution of the problem. Petkovi6 and Jonker proposed a solution that allows a system to be applied in multiple domains with minimal adjustments. They also designed and described a prototype video database management system, which is based on techniques they proposed in the book.
 

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Contents

Introduction
1
2 Video Retrieval from a Data Management Perspective
3
3 Research Approach
5
4 Outline of the Book
6
5 Main Contributions
8
Database Management Systems and ConetentBased Retrieval
9
2 Databases
10
3 Information Retrieval
18
Stochastic Modeling of Video Events
73
2 Hidden Markov Models
75
3 Bayesian Networks
78
4 Back to the Tennis Case Study
80
5 Formula 1 Case Study
89
6 Summary
104
Cobra A prototype of a Video DBMS
109
2 Architecture of the Cobra VDBMS
110

4 ContentBased Video Retreival
19
5 Summary
30
Video Modeling
33
2 CoarseGrained Structuring
34
3 FineGrained Interpretation
38
4 Discussion
43
5 Cobra Video Modeling Framework
44
6 Tennis Case Study
47
7 Summary
50
SpatioTemporal Formalization of Video Events
55
2 SpatioTemporal Extension of the Cobra Framework
56
3 Tennis Case Study Revisited
62
4 Summary
69
3 Implementation Platform
114
4 Dynamic Feature Extraction
116
5 offLine Metadata Extraction Using Feature Grammars
117
6 SpatioTemporal Extension
120
7 HMM Integration
124
8 Integrated Querying
127
9 Integrated Contentand ContentBased Search
131
10 Summary
136
Conclusions
141
2 Recommendations for Future Research
147
About the Authors
149
Index
151
Copyright

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