Abstract
Content-Based Video Copy Detection (CBVCD) aims at detecting whether or not a query video is a copy or part of a reference video from database. In this paper, we present a CBVCD system based on spatio-temporal features that can competitively deal with large database in terms of both performance and efficiency. Instead of selecting keyframes or uniformly sampling from original videos and then extracting global or local visual features for frames, we first divide a video into segments with fixed length and then extract 3D spatio-temporal features for the whole segment. After that, we perform similarity search comparing all the reference segments with query segments and apply a copy verifying to decide the final copy detection result. The experimental results on the TRECVID 2011 video copy detection dataset show that the proposed system is effective and efficient.
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Hu, R., Li, B., Hu, W., Yang, J. (2013). Spatio-temporal Features for Efficient Video Copy Detection. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_16
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DOI: https://doi.org/10.1007/978-3-642-42057-3_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
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