Detecting Repeats for Video Structuring

Xavier Naturel 1 Patrick Gros 1, *
* Corresponding author
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Television daily produces massive amounts of videos. Digital video is unfortunately an unstructured document in which it is very difficult to find any information. Television streams have however a strong and stable but hidden structure that we want to discover by detecting repeating objects in the video stream. This paper shows that television streams are actually highly redundant and that detecting repeats can be an effective way to detect the underlying structure of the video. A method for detecting these repetitions is presented here with an emphasis on the efficiency of the search in a large video corpus. Very good results are obtained both in terms of effectiveness (98% in recall and precision) as well as efficiency since one day of video is queried against a 3 weeks dataset in only 1 s.
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Journal articles
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https://hal.inria.fr/inria-00568177
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Submitted on : Tuesday, February 22, 2011 - 5:34:57 PM
Last modification on : Friday, November 16, 2018 - 1:22:34 AM

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Xavier Naturel, Patrick Gros. Detecting Repeats for Video Structuring. Multimedia Tools and Applications, Springer Verlag, 2008, 38 (2), pp.233-252. ⟨http://www.springerlink.com/content/f417v67462m89067/fulltext.pdf⟩. ⟨10.1007/s11042-007-0180-1⟩. ⟨inria-00568177⟩

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