Paper
13 March 1996 Comparison of video shot boundary detection techniques
John S. Boreczky, Lawrence A. Rowe
Author Affiliations +
Proceedings Volume 2670, Storage and Retrieval for Still Image and Video Databases IV; (1996) https://doi.org/10.1117/12.234794
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States
Abstract
Many algorithms have been proposed for detecting video shot boundaries and classifying shot and shot transition types. Few published studies compare available algorithms, and those that do have looked at limited range of test material. This paper presents a comparison of several shot boundary detection and classification techniques and their variations including histograms, discrete cosine transform, motion vector, and block matching methods. The performance and ease of selecting good thresholds for these algorithms are evaluated based on a wide variety of video sequences with a good mix of transition types. Threshold selection requires a trade-off between recall and precision that must be guided by the target application.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John S. Boreczky and Lawrence A. Rowe "Comparison of video shot boundary detection techniques", Proc. SPIE 2670, Storage and Retrieval for Still Image and Video Databases IV, (13 March 1996); https://doi.org/10.1117/12.234794
Lens.org Logo
CITATIONS
Cited by 188 scholarly publications and 6 patents.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Video

Detection and tracking algorithms

Cameras

Televisions

Colorimetry

Image processing

Video compression

RELATED CONTENT

Detection of camera operations in compressed video sequences
Proceedings of SPIE (January 15 1997)
Video segmentation for post-production
Proceedings of SPIE (December 19 2001)
Semiautomatic dynamic video object marker creation
Proceedings of SPIE (December 17 1998)
Recognition by means of reduced algorithm
Proceedings of SPIE (April 29 2008)

Back to Top