Abstract
Video classification provides an efficient way to manage and utilize the video data. Existing works on this topic fall into this category: enlarging the feature set until the classification is reliable enough. However, some features may be redundant or irrelevant. In this paper, we address the problem of choosing efficient feature set in video genre classification to achieve acceptable classification results but relieve computation burden significantly. A rough set approach is proposed. In comparison with existing works and the decision tree method, experimental results verify the efficiency of the proposed approach.
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© 2006 Springer-Verlag Berlin Heidelberg
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Cheng, W., Liu, C., Wang, X. (2006). A Rough Set Approach to Video Genre Classification. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_110
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DOI: https://doi.org/10.1007/11864349_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44630-9
Online ISBN: 978-3-540-44632-3
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