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Encyclopedia of Database Systems pp 601–609Cite as

Content-Based Video Retrieval

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Synonyms

Digital video retrieval; Digital video search

Definition

Content-based Video Retrieval refers to the provision of search facilities over archives of digital video content, where these search facilities are based on the outcome of an analysis of digital video content to extract indexable data for the search process.

Historical Background

As the volume of digital video data in existence constantly increases, the resulting vast archives of professional video content and UCC (User Created Content) are presenting an opportunity for the development of content-based video retrieval systems. Content-based video retrieval system development was initially lead by academic research such as the Informedia Digital Video Library [3] from CMU and the Físchlár Digital Video Suite [6] from DCU (Dublin City University). Both of these systems operated over thousands of hours of content, however digital video search has now become an everyday WWW phenomenon, with millions of items of digital...

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Recommended Reading

  1. Browne P, Smeaton AF, Murphy N, O’Connor N, Marlow S, Berrut C. Evaluating and combining digital video shot boundary detection algorithms. In: Proceedings of the IMVIP 2000 – Irish Machine Vision and Image Processing Conference; 2000. p. 93–100.

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  2. Christel MG, Hauptmann AG, Wactlar HD, Ng TD. Collages as dynamic summaries for news video. In: Proceedings of the 10th ACM International Conference on Multimedia; 2002. p. 561–9.

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  3. Hauptmann A. Lessons for the future from a decade of informedia video analysis research, image and video retrieval. In: Proceedings of the 4th International Conference Image and Video Retrieval; 2005. p. 1–10.

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  4. Sadlier D, O’Connor N. Event detection in field sports video using audio-visual features and a support vector machine. IEEE Trans Circuits Syst Video Technol. 2005;15(10):1225–33.

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  5. Sivic J, Zisserman A. Video Google: a text retrieval approach to object matching in videos. In: Proceedings of the 9th IEEE Conference Computer Vision; 2003. p. 1470–7.

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  6. Smeaton AF, Lee H, Mc Donald K. Experiences of creating four video library collections with the Físchlár system. Int J Digit Libr. 2004;4(1):42–4.

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  7. Smeaton AF, Over P, Kraaij W. Evaluation campaigns and TRECVid. In: Proceedings of the 8th ACM SIGMM International Workshop on Multimedia Information Retrieval; 2006. p. 321–30.

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  8. http://trec.nist.gov Last visited June ’08.

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Author information

Authors and Affiliations

  1. Dublin City University, Dublin, Ireland

    Cathal Gurrin

Authors
  1. Cathal Gurrin
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Corresponding author

Correspondence to Cathal Gurrin .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. Dept. of Computer Science, New Jersey Inst. of Technology, Newark, NJ, USA

    Vincent Oria

  2. Digital Content and Media Sciences ReseaMultimedia Information Research Division, National Institute of Informatics, Tokyo, Japan

    Shin'ichi Satoh

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Gurrin, C. (2018). Content-Based Video Retrieval. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1027

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