Content-Based Analysis of Digital Video

by Alan Hanjalic

Overview

Content-Based Analysis Of Digital Video focuses on fundamental issues underlying the development of content access mechanisms for digital video. It treats topics that are critical to successfully automating the video content extraction and retrieval processes, and includes coverage of:

- Video parsing,

- Video content indexing and representation,

- Affective video content analysis.

In this well illustrated book the author integrates related information currently scattered throughout the literature and combines it with new ideas into a unified theoretical approach to video content analysis. The material also suggests ideas for future research.

Systems developers, researchers and students working in the area of content-based analysis and retrieval of video and multimedia in general will find this book invaluable.

Product Details

ISBN-13:	9781402081149
Publisher:	Springer US
Publication date:	07/21/2004
Edition description:	2004
Pages:	193
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

About the Author

Dr. Hanjalic is an Assistant Professor in the Department of Mediamatics at Delft University of Technology, The Netherlands. He was a visiting scientist at Hewlett-Packard Labs in Palo Alto, CA, a visiting scientist at Philips Research Labs in NY as well as a research fellow at British Telecom Labs (now BTExact), Adastral Park , UK. His research interests are in the broad area of multimedia signal processing, with focus on video content analysis for interactive browsing, retrieval and personalized (on-demand) video delivery.

Table of Contents

Preface. Acknowledgements. Introduction. 1.1. Video content analysis: toward a paradigm shift. 1.2. Toward the meaning of video data: bridging the semantic gap. 1.3. Book objective, scope and overview. Detecting shot boundaries in video. 2.1. Introduction. 2.2. Shot-boundary detection: unraveling the problem. 2.3. Feature extraction. 2.4. Modeling prior information. 2.5. Modeling discriminative information. 2.6. Bayesian approach to decision module design. 2.7. Remarks and recommendations. 2.8. References and further reading. Parsing a video into semantic segments. 3.1. Introduction. 3.2. The principle of content coherence. 3.3. Video parsing based on the content coherence principle. 3.4. Content similarity between clips. 3.5. Audio-assisted video parsing. 3.6. Remarks and recommendations. 3.7. References and further reading. Video indexing and abstraction for retrieval. 4.1. Introduction. 4.2. Video indexing. 4.3. Video content representation for browsing and content preview. 4.4. Remarks and recommendations. 4.5. References and further reading. Affective video content analysis. 5.1. Introduction. 5.2. Dimensional approach to affect. 5.3. Affective video content representation. 5.4. Affective video content modeling. 5.5. Applications. 5.6. Remarks and recommendations. 5.7. References and further reading.