Abstract
Automatic shot detection is the first step and also an important step for content-based parsing and indexing of video data. Many methods have been introduced to address this problem, e.g. pixel-by-pixel comparisons and histogram comparisons. But gray or color histograms used in most existing methods ignore the problem of illumination variation inherent in the video production process. So they often fail when the incident illumination varies. And because shot change is basically a local process of a video, it is difficult to find an appropriate global threshold for absolute difference measure. In this paper, new techniques for shot detection are proposed. We use color ratio histograms as frame content measure, because it is robust to illumination changes. A local adaptive threshold technique is adopted to utilize the local characteristic of shot change. The effectiveness of our methods is validated by experiments on some real-world video sequences. Some experimental results are also discussed in this paper.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
J.S. Boreczky and L.A. Rowe, Comparison of video shot boundary detection techniques, Proc. SPIE Conf. Storage and Retrieval for Image and Video Databases IV, Vol. 2670, pp170–179, 1996.
K. Otsuji, Y. Tonomura and Y. Ohba, Video browsing using brightness data, Proc. SPIE Conf. Visual Communications and Image Processing, pp.980–989, November 1991.
A. Nagasaka and Y. Tanaka, Automatic video indexing and full-video search for object appearances, Proc. 2nd Visual Database Systems, pp119–133, October 1991
H. Zhang, A. Kankanhalli, and S. Smoliar, Automatic partitioning of full-motion video, Multimedia Systems, vol. 1, pp10–28, 1993
I.K. Sethi, N. Patel, A Statistical Approch to Scene Change Detection, Proc. SPIE Conf. Storage and Retrieval for Image and Video Databases, vol.2420, pp329–338, 1995
J. Wei, M.S. Drew, and Z.-N. Li, Illumination-invariant video segmentation by hierarchical robust thresholding, Proc. SPIE Conf. Storage and Retrieval for Image and Video Databases, vol.3312, pp.188–201,1998
B.V. Funt and G.D. Finlayson, Color Constant color indexing, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17, pp522–529, 1995
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kong, W., Ding, X., Lu, H., Ma, S. (1999). Improvement of Shot Detection Using Illumination Invariant Metric and Dynamic Threshold Selection. In: Huijsmans, D.P., Smeulders, A.W.M. (eds) Visual Information and Information Systems. VISUAL 1999. Lecture Notes in Computer Science, vol 1614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48762-X_35
Download citation
DOI: https://doi.org/10.1007/3-540-48762-X_35
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66079-8
Online ISBN: 978-3-540-48762-3
eBook Packages: Springer Book Archive