Content-Based Video Retrieval: A Database PerspectiveSpringer Science & Business Media, 2013. gada 29. jūn. - 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. |
No grāmatas satura
1.–5. rezultāts no 29.
. lappuse
... Architecture of the Cobra VDBMS 110 3 . Implementation Platform 114 4 . Dynamic Feature Extraction 116 5 . Off - Line Metadata Extraction Using Feature Grammars 117 6 . Spatio - Temporal Extension 120 7 . HMM Integration 124 8 ...
... Architecture of the Cobra VDBMS 110 3 . Implementation Platform 114 4 . Dynamic Feature Extraction 116 5 . Off - Line Metadata Extraction Using Feature Grammars 117 6 . Spatio - Temporal Extension 120 7 . HMM Integration 124 8 ...
4. lappuse
... architecture , system implementation and query processing issues need to be considered to achieve the main goal . The following sections explain further the main requirements a database management system has to accomplish to support ...
... architecture , system implementation and query processing issues need to be considered to achieve the main goal . The following sections explain further the main requirements a database management system has to accomplish to support ...
5. lappuse
... architecture and implementation The next objective of this book is to investigate specific issues regarding the architecture and implementation of a video retrieval system . Given our starting point , we discuss the integration of the ...
... architecture and implementation The next objective of this book is to investigate specific issues regarding the architecture and implementation of a video retrieval system . Given our starting point , we discuss the integration of the ...
6. lappuse
... architecture and implementation issues , we propose a design of a content - based video retrieval system . In order to be able to integrate the techniques used for content extraction in an efficient and flexible manner , we choose for ...
... architecture and implementation issues , we propose a design of a content - based video retrieval system . In order to be able to integrate the techniques used for content extraction in an efficient and flexible manner , we choose for ...
7. lappuse
... architecture for content - based video retrieval system . Furthermore , the chapter describes the implementation platform that the system is built on . It illustrates how a database management system can be extended to support feature ...
... architecture for content - based video retrieval system . Furthermore , the chapter describes the implementation platform that the system is built on . It illustrates how a database management system can be extended to support feature ...
Saturs
Database Management Systems and ConetentBased Retrieval | 31 |
SpatioTemporal Formalization of Video Events 55 | 54 |
Stochastic Modeling of Video Events | 73 |
A Prototype of a Video DBMS 109 | 108 |
Conclusions | 141 |
About the Authors | 149 |
Citi izdevumi - Skatīt visu
Content-Based Video Retrieval: A Database Perspective Milan Petković,Willem Jonker Ierobežota priekšskatīšana - 2003 |
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 Australian Open 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 frames hidden Markov models high-level concepts highlights implementation information retrieval integrated interface intervals low-level features Matlab metadata MFCC Michael Schumacher modalities Monet movie Multimedia net-playing object grammar objects and events observation sequence operations performed physical level pixels prototype quantization query language raw video data regions schema Section semantic content semantic gap spatial spatio-temporal formalization specific stroke recognition techniques temporal relations Tennis case study tennis videos video content video database management video documents video modeling video objects video processing video retrieval system video sequences video structure video units