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

Authors:

Georges Quénot,

Jenny Benois-Pineau,

Boris Mansencal,

Frederic Precioso,

Patrick Lambert,

Bertrand Augereau,

Lionel Granjon,

Denis Pellerin,

Michèle Rombaut,

Stephane AyacheAuthors Info & Claims

TVS '08: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop

Pages 80 - 84

https://doi.org/10.1145/1463563.1463577

Published: 31 October 2008 Publication History

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

References

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Cited By

Chek ZSee JHo JLau PPillai PGouet-Brunet VKosti RWeng L(2024)Understanding User Engagement in Museum TikTok Videos by Exploring Multimodal CuesProceedings of the 6th workshop on the analySis, Understanding and proMotion of heritAge Contents10.1145/3689094.3689467(41-49)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3689094.3689467
Bhaumik HBhattacharyya SChakraborty S(2017)Content Coverage and Redundancy Removal in Video SummarizationIntelligent Analysis of Multimedia Information10.4018/978-1-5225-0498-6.ch013(352-374)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-0498-6.ch013
Sargent GPerez-Daniel KStoian ABenois-Pineau JMaabout SNicolas HMiyatake MCarrive J(2016)A scalable summary generation method based on cross-modal consensus clustering and OLAP cube modelingMultimedia Tools and Applications10.1007/s11042-015-2863-375:15(9073-9094)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s11042-015-2863-3
Show More Cited By

Index Terms

Rushes summarization by IRIM consortium: redundancy removal and multi-feature fusion
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

cover image ACM Conferences

TVS '08: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop

October 2008

156 pages

ISBN:9781605583099

DOI:10.1145/1463563

General Chairs:
Paul Over
NIST, USA
,
Alan F. Smeaton
Dublin City University, Ireland

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 31 October 2008

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October 31, 2008

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Cited By

Chek ZSee JHo JLau PPillai PGouet-Brunet VKosti RWeng L(2024)Understanding User Engagement in Museum TikTok Videos by Exploring Multimodal CuesProceedings of the 6th workshop on the analySis, Understanding and proMotion of heritAge Contents10.1145/3689094.3689467(41-49)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3689094.3689467
Bhaumik HBhattacharyya SChakraborty S(2017)Content Coverage and Redundancy Removal in Video SummarizationIntelligent Analysis of Multimedia Information10.4018/978-1-5225-0498-6.ch013(352-374)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-0498-6.ch013
Sargent GPerez-Daniel KStoian ABenois-Pineau JMaabout SNicolas HMiyatake MCarrive J(2016)A scalable summary generation method based on cross-modal consensus clustering and OLAP cube modelingMultimedia Tools and Applications10.1007/s11042-015-2863-375:15(9073-9094)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s11042-015-2863-3
Ferreira LCruz LAssuncao P(2015)A generic framework for optimal 2D/3D key-frame extraction driven by aggregated saliency mapsImage Communication10.1016/j.image.2015.09.00539:PA(98-110)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1016/j.image.2015.09.005
Karaman SBenois-Pineau JDovgalecs VMégret RPinquier JAndré-Obrecht RGaëstel YDartigues J(2014)Hierarchical Hidden Markov Model in detecting activities of daily living in wearable videos for studies of dementiaMultimedia Tools and Applications10.1007/s11042-012-1117-x69:3(743-771)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1007/s11042-012-1117-x
Liu NDellandréa ETellez BChen L(2014)A Selective Weighted Late Fusion for Visual Concept RecognitionFusion in Computer Vision10.1007/978-3-319-05696-8_1(1-28)Online publication date: 26-Mar-2014
https://doi.org/10.1007/978-3-319-05696-8_1
Karaman SBenois-Pineau JMegret RPinquier JGaestel YDartigues J(2011)Activities of daily living indexing by hierarchical HMM for dementia diagnostics2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI)10.1109/CBMI.2011.5972524(79-84)Online publication date: Jun-2011
https://doi.org/10.1109/CBMI.2011.5972524
Over PSmeaton AAwad GOver PSmeaton A(2008)The trecvid 2008 BBC rushes summarization evaluationProceedings of the 2nd ACM TRECVid Video Summarization Workshop10.1145/1463563.1463564(1-20)Online publication date: 31-Oct-2008
https://dl.acm.org/doi/10.1145/1463563.1463564

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