Rushes summarization by IRIM consortium: redundancy removal and multi-feature fusion

  • Authors:

  • Georges Quénot;Jenny Benois-Pineau;Boris Mansencal;Eliana Rossi;Matthieu Cord;Frederic Precioso;David Gorisse;Patrick Lambert;Bertrand Augereau;Lionel Granjon;Denis Pellerin;Michèle Rombaut;Stephane Ayache

  • Affiliations:

  • LIG UMR CNRS/INP/INRIA/University Josepf Fourier/UPMF, Grenoble, France;Université Bordeaux, Talence, France;LABRI UMR Université Bordeaux, Talence, France;Université Bordeaux, Talence, France and University of Brescia, Italy;LIP6 UMR CNRS/University Pierre et Marie Curie, Paris, France;ETIS UMR CNRS/ENSEA, Cergy-Pontoise, France;ETIS UMR CNRS/ENSEA, Cergy-Pontoise, France;LISTIC EA CNRS/Polytech de Savoie, Annecy le Vieux, France;XLIM-SIC UMR CNRS/University of Poitiers, Poitiers, France;GIPSA-LAB UMR CNRS/INPG, ST. Martin d'Heres, France;GIPSA-LAB UMR CNRS/INPG, St. Martin d'Heres, France;GIPSA-LAB UMR CNRS/INPG, St. Martin d'Heres, France;LIRIS UMR CNRS/INSA de Lyon/Université Claude Bernard Lyon 1/Université Lumière Lyon 2/Ecole Centrale de Lyon, Villeurbanne, France

  • Venue:
  • TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
  • Year:
  • 2008

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Abstract

In this paper, we present the first participation of a consortium of French laboratories, IRIM, to the TRECVID 2008 BBC Rushes Summarization task. Our approach resorts to video skimming. We propose two methods to reduce redundancy, as rushes include several takes of scenes. We also take into account low and mid-level semantic features in an ad-hoc fusion method in order to retain only significant content