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Finding the optimal temporal partitioning of video sequences

Truong, Ba Tu and Venkatesh, Svetha 2005, Finding the optimal temporal partitioning of video sequences, in ICME 2005 : Proceedings of the IEEE International Conference on Multimedia and Expo 2005, IEEE, Piscataway, N. J., pp. 1182-1185.

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Title Finding the optimal temporal partitioning of video sequences
Author(s) Truong, Ba Tu
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name IEEE International Conference on Multimedia and Expo (2005 : Amsterdam, The Netherlands)
Conference location Amsterdam, The Netherland
Conference dates 6-8 Jul. 2005
Title of proceedings ICME 2005 : Proceedings of the IEEE International Conference on Multimedia and Expo 2005
Editor(s) [Unknown]
Publication date 2005
Conference series IEEE International Conference on Multimedia and Expo
Start page 1182
End page 1185
Total pages 4
Publisher IEEE
Place of publication Piscataway, N. J.
Summary The existing techniques for shot partitioning either process each shot boundary independently or proceed sequentially. The sequential process assumes the last shot boundary is correctly detected and utilizes the shot length distribution to adapt the threshold for detecting the next boundary. These techniques are only locally optimal and suffer from the strong assumption about the correct detection of the last boundary. Addressing these fundamental issues, in this paper, we aim to find the global optimal shot partitioning by utilizing Bayesian principles to model the probability of a particular video partition being the shot partition. A computationally efficient algorithm based on Dynamic Programming is then formulated. The experimental results on a large movie set show that our algorithm performs consistently better than the best adaptive-thresholding technique commonly used for the task.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0780393325
9780780393325
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2005, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044627

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.