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Evaluation campaigns and TRECVid
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Proceedings of the 8th ACM international workshop on Multimedia information retrieval table of contents
Santa Barbara, California, USA
SESSION: Special session 2: query systems for data retrieval in large personal image and video databases table of contents
Pages: 321 - 330  
Year of Publication: 2006
ISBN:1-59593-495-2
Authors
Alan F. Smeaton  Dublin City University, Dublin, Ireland
Paul Over  National Institute of Standards and Technology, Gaithersburg, MD
Wessel Kraaij  TNO Information and Communication Technology, The Netherlands
Sponsors
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
Publisher
ACM  New York, NY, USA
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ABSTRACT

The TREC Video Retrieval Evaluation (TRECVid)is an international benchmarking activity to encourage research in video information retrieval by providing a large test collection, uniform scoring procedures, and a forum for organizations 1 interested in comparing their results. TRECVid completed its fifth annual cycle at the end of 2005 and in 2006 TRECVid will involve almost 70 research organizations, universities and other consortia. Throughout its existence, TRECVid has benchmarked both interactive and automatic/manual searching for shots from within a video corpus,automatic detection of a variety of semantic and low-level video features, shot boundary detection and the detection of story boundaries in broadcast TV news. This paper will give an introduction to information retrieval (IR) evaluation from both a user and a system perspective, high-lighting that system evaluation is by far the most prevalent type of evaluation carried out. We also include a summary of TRECVid as an example of a system evaluation bench-marking campaign and this allows us to discuss whether such campaigns are a good thing or a bad thing. There are arguments for and against these campaigns and we present some of them in the paper concluding that on balance they have had a very positive impact on research progress.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
AMI: Augmented Multi-Person Interaction. URL:www.amiproject.org/,Last checked 21 June 2006.
 
2
ARGOS: Evaluation Campaign for Surveillance Tools of Video Content. URL: www.irit.fr/recherches/SAMOVA/MEMBERS/-JOLY/argos/, Last checked 21 June 2006.
 
3
CLEAR '06 Evaluation Campaign and Workshop - Classification of Events, Activities and Relationships. URL:www.clear-evaluation.org/,Last checked 21 June 2006.
 
4
ETISEO: Video Understanding Evaluation. URL:www.silogic.fr/etiseo/, June 2006.
 
5
Face Recognition Grand Challenge. URL:www.frvt.org/FRGC,2006.
 
6
INEX:INitiative for the Evaluation of XML Retrieval. URL:inex.is.informatik.uni-duisburg.de/,Last checked 21 June 2006.
 
7
The Internet Archive Movie Archive home page. URL:www.archive.org/movies, 2006.
 
8
Lscom lexicon definitions and annotations. URL:www.ee.columbia.edu/dvmm/lscom, 2006.
 
9
NTCIR:NII Test Collection for IR Systems Project. URL:research.nii.ac.jp/ntcir/, Last checked June 2006.
 
10
PETS 2006: Ninth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance. URL:www.pets2006.net/,Last checked 21 June 2006.
 
11
The Benchathlon Network: Home of CBIR Benchmarking. URL:www.benchathlon.net/, Last checked 21 June 2006.
 
12
The Cross-Language Evaluation Forum (CLEF). URL:clef.isti.cnr.it/, Last checked 21 June 2006.
 
13
The IMAG-EVAL Evaluation Campaign. URL:www.imageval.org/, Last checked 21 June 2006.
 
14
M. G. Christel and A. G. Hauptmann. The Use and Utility of High-Level Semantic Features in Video Retrieval. In Proceedings of the International Conference on Video Retrieval pages 134--144, Singapore, 20-22 July 2005.
 
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B. F. J. Manly. Randomization, Bootstrap, and Monte Carlo Methods in Biology Chapman & Hall, London, UK, 2nd edition, 1997.
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20
NIST. TREC Video Retrieval Evaluation Publications. URL: www-nlpir.nist.gov/projects/tvpubs/tv.pubs.org.html,2006.
 
21
R. Ruiloba, P. Joly, S. Marchand-Maillet, and G. Quénot. Towards a Standard Protocol for the Evaluation of Video-to-Shots Segmentation Algorithms. In European Workshop on Content Based Multimedia Indexing Toulouse, France, October 1999.
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CITED BY  60

Collaborative Colleagues:
Alan F. Smeaton: colleagues
Paul Over: colleagues
Wessel Kraaij: colleagues