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Video genre categorization using audio wavelet coefficients
conference contribution
posted on 2002-01-01, 00:00 authored by P Dinh, C Dorai, Svetha VenkateshSvetha VenkateshIn this paper, we investigate the use of a wavelet transform-based analysis of audio tracks accompanying videos for the problem of automatic program genre detection. We compare the classification performance based on wavelet-based audio features to that using conventional features derived from Fourier and time analysis for the task of discriminating TV programs such as news, commercials, music shows, concerts, motor racing games, and animated cartoons. Three different classifiers namely the Decision Trees, SVMs, and k-Nearest Neighbours are studied to analyse the reliability of the performance of our wavelet features based approach. Further, we investigate the issue of an appropriate duration of an audio clip to be analyzed for this automatic genre determination. Our experimental results show that features derived from the wavelet transform of the audio signal can very well separate the six video genres studied. It is also found that there is no significant difference in performance with varying audio clip durations across the classifiers.
History
Event
Asian Conference on Computer Vision (5th : 2002 : Melbourne, Vic.)Pagination
69 - 74Publisher
Asian Federation of Computer Vision SocietiesLocation
Melbourne, Vic.Place of publication
[Tokyo, Japan]Start date
2002-01-22End date
2002-01-25ISBN-13
9780958025607ISBN-10
0958025606Language
engNotes
Papers will be published in Springer's Lecture Notes in Computer Science.Publication classification
E1.1 Full written paper - refereedCopyright notice
2002, SpringerEditor/Contributor(s)
D Suter, A Bab-HadiasharTitle of proceedings
ACCV 2002 : Proceedings of the 5th Asian Conference on Computer VisionUsage metrics
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