Detecting Repeats for Video Structuring

Xavier Naturel 1 Patrick Gros 1, *
* Auteur correspondant
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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Article dans une revue
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〉
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https://hal.inria.fr/inria-00568177
Contributeur : Patrick Gros <>
Soumis le : mardi 22 février 2011 - 17:34:57
Dernière modification le : vendredi 16 novembre 2018 - 01:22:34

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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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