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Automatic genre identification for content-based video categorization

Truong, Ba Tu, Venkatesh, Svetha and Dorai, Chitra 2000, Automatic genre identification for content-based video categorization, in ICPR 2000 : Proceedings of the International Conference on Pattern Recognition, IEEE, Washington, D. C., pp. 230-233.

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Title Automatic genre identification for content-based video categorization
Author(s) Truong, Ba Tu
Venkatesh, Svetha
Dorai, Chitra
Conference name International Conference on Pattern Recognition (15th : 2000 : Barcelona, Spain)
Conference location Barcelona, Spain
Conference dates 3-8 Sep. 2000
Title of proceedings ICPR 2000 : Proceedings of the International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2000
Conference series International Conference on Pattern Recognition
Start page 230
End page 233
Total pages 4
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) computer science
data mining
feature extraction
gunshot detection systems
humans
motion pictures
music information retrieval
TV
video sequences
Summary This paper presents a set of computational features originating from our study of editing effects, motion, and color used in videos, for the task of automatic video categorization. These features besides representing human understanding of typical attributes of different video genres, are also inspired by the techniques and rules used by many directors to endow specific characteristics to a genre-program which lead to certain emotional impact on viewers. We propose new features whilst also employing traditionally used ones for classification. This research, goes beyond the existing work with a systematic analysis of trends exhibited by each of our features in genres such as cartoons, commercials, music, news, and sports, and it enables an understanding of the similarities, dissimilarities, and also likely confusion between genres. Classification results from our experiments on several hours of video establish the usefulness of this feature set. We also explore the issue of video clip duration required to achieve reliable genre identification and demonstrate its impact on classification accuracy.
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 0769507506
ISSN 1051-4651
Language eng
Field of Research 109999 Technology not elsewhere classified
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2000, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044540

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.