Scene-based scalable video summarization

Ying Li; C. C. Jay Kuo; Daniel Tretter

doi:10.1117/12.511559

26 November 2003 Scene-based scalable video summarization

Ying Li, C. C. Jay Kuo, Daniel Tretter

Proceedings Volume 5242, Internet Multimedia Management Systems IV; (2003) https://doi.org/10.1117/12.511559
Event: ITCom 2003, 2003, Orlando, Florida, United States

Abstract

A scalable video summarization and navigation system is proposed in this work. Particularly, given the desired number of keyframes for a video sequence, we first distribute it among underlying video scenes and sinks based on their respective importance ranks. Then, we select the most important shot of each sink as its R-shot and further assign each sink's designated number of keyframes to its R-shot. Finally, a time-constrained keyframe extraction scheme is developed to locate all keyframes. Consequently, we can achieve a scalable video summary from the initial keyframe set by exploiting such a video structure-based ranking scheme. In addition, a content navigation tool is also developed which could help users freely access or locate specific video scenes or shots. Sophisticated user studies have shown that this summarization and navigation system can not only help users quickly browse video content, but also assist them in searching for particular video segments.

Citation Download Citation

Ying Li, C. C. Jay Kuo, and Daniel Tretter "Scene-based scalable video summarization", Proc. SPIE 5242, Internet Multimedia Management Systems IV, (26 November 2003); https://doi.org/10.1117/12.511559

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available