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Content-based video classification using support vector machines
Vakkalanka Gowri Suresh
Published in
2004
Volume: 3316
   
Pages: 726 - 731
Abstract
In this paper, we investigate the problem of video classification into predefined genre. The approach adopted is based on spatial and temporal descriptors derived from short video sequences (20 seconds). By using support vector machines (SVMs), we propose an optimized multiclass classification method. Five popular TV broadcast genre namely cartoon, commercials, cricket, football and tennis are studied. We tested our scheme on more than 2 hours of video data and achieved an accuracy of 92.5%. © Springer-Verlag Berlin Heidelberg 2004.
About the journal
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN03029743
Open AccessNo
Concepts (14)
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    Classification (of information)
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    Image classification
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    SPORTS
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    Television broadcasting
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    Video recording
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    Content-based
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    Descriptors
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    MULTICLASS CLASSIFICATION METHODS
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    Support vector machine (svms)
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    TV BROADCAST
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    VIDEO CLASSIFICATION
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    Video data
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    Video sequences
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    Support vector machines