Browse DORAS
Browse Theses
Search
Latest Additions
Creative Commons License
Except where otherwise noted, content on this site is licensed for use under a:

Content-based video retrieval: three example systems from TRECVid

Smeaton, Alan F. and Wilkins, Peter and Worring, Marcel and de Rooij, Ork and Chua, Tat-Seng and Luan, Huanbo (2008) Content-based video retrieval: three example systems from TRECVid. International Journal of Imaging Systems and Technology, 18 (2-3). pp. 195-201. ISSN 0899-9457

Full text available as:

[img]
Preview
PDF (pre-peer reviewed version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
654Kb
[img]PDF (final peer-reviewed version) - Registered users only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
161Kb

Abstract

The growth in available online video material over the internet is generally combined with user-assigned tags or content description, which is the mechanism by which we then access such video. However, user-assigned tags have limitations for retrieval and often we want access where the content of the video itself is directly matched against a user’s query rather than against some manually assigned surrogate tag. Content-based video retrieval techniques are not yet scalable enough to allow interactive searching on internet-scale, but the techniques are proving robust and effective for smaller collections. In this paper we show 3 exemplar systems which demonstrate the state of the art in interactive, content-based retrieval of video shots, and these three are just three of the more than 20 systems developed for the 2007 iteration of the annual TRECVid benchmarking activity. The contribution of our paper is to show that retrieving from video using content-based methods is now viable, that it works, and that there are many systems which now do this, such as the three outlined herein. These systems, and others can provide effective search on hundreds of hours of video content and are samples of the kind of content-based search functionality we can expect to see on larger video archives when issues of scale are addressed.

Item Type:Article (Published)
Refereed:No
Subjects:Computer Science > Information storage and retrieval systems
Computer Science > Information retrieval
Computer Science > Digital video
DCU Faculties and Centres:Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:John Wiley & Sons
Official URL:http://dx.doi.org/10.1002/ima.v18:2/3
Copyright Information:This is the pre-peer reviewed version of the following article: Smeaton, Alan F. and Wilkins, Peter and Worring, Marcel and de Rooij, Ork and Chua, Tat-Seng and Luan, Huanbo (2008) Content-based video retrieval: three example systems from TRECVid. International Journal of Imaging Systems and Technology, 18 (2-3). pp. 195-201. ISSN 0899-9457, which has been published in final form at http://dx.doi.org/10.1002/ima.v18:2/3
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland
ID Code:620
Deposited On:29 Oct 2008 11:35 by Alan F. Smeaton. Last Modified 12 Nov 2010 12:25

Archive Staff Only: edit this record