Content-Based Video Retrieval: A Database PerspectiveSpringer Science & Business Media, 2003. gada 31. okt. - 151 lappuses 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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Saturs
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 |
151 | |
Citi izdevumi - Skatīt visu
Content-Based Video Retrieval: A Database Perspective Milan Petkovic,Willem Jonker Ierobežota priekšskatīšana - 2013 |
Content-Based Video Retrieval: A Database Perspective Milan Petković,Willem Jonker Priekšskatījums nav pieejams - 2013 |
Content-Based Video Retrieval: A Database Perspective Milan Petković,Willem Jonker Priekšskatījums nav pieejams - 2010 |
Bieži izmantoti vārdi un frāzes
algebra algorithm annotation approach architecture audio-visual automatic extraction chapter Cobra framework Cobra video codebook color histogram computer vision content-based retrieval content-based video retrieval data model database management system database system DBMS defined described detection dominant color dynamic Bayesian networks evaluation event descriptions event type example feature extraction feature grammar hidden Markov models high-level concepts highlights implementation information retrieval integrated interface Jonker low-level features Matlab metadata MFCC Michael Schumacher modalities Monet movie Multimedia net-playing object grammar objects and events observation sequence operations performed Petkovic physical level pixels preprocessor prototype quantization query language raw video data regions schema Section semantic content semantic gap spatial spatio-temporal formalization specific stroke recognition techniques temporal dependencies temporal relations Tennis case study tennis videos tuple video content video database management video documents video events video objects video processing video retrieval system video sequences video structure video units
Atsauces uz šo grāmatu
Adaptive Multimedia Retrieval: User, Context, and Feedback: Third ... Marcin Detyniecki,Joemon M. Jose,Andreas Nürnberger,C. J. van Rijsbergen Ierobežota priekšskatīšana - 2006 |