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Establishing the utility of non-text search for news video retrieval with real world users
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Source
International Multimedia Conference archive
Proceedings of the 15th international conference on Multimedia table of contents
Augsburg, Germany
SESSION: Applications 4 - helping end users table of contents
Pages: 707 - 716  
Year of Publication: 2007
ISBN:978-1-59593-702-5
Author
Michael G. Christel  Carnegie Mellon University, Pittsburgh, PA
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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Downloads (6 Weeks): 9,   Downloads (12 Months): 71,   Citation Count: 4
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ABSTRACT

TRECVID participants have enjoyed consistent success using storyboard interfaces for shot-based retrieval, as measured by TRECVID interactive search mean average precision (MAP). However, much is lost by only looking at MAP, and especially by neglecting to bring in representatives of the target user communities to conduct such tasks. This paper reports on the use of within-subjects experiments to reduce subject variability and emphasize the examination of specific video search interface features for their effectiveness in interactive retrieval and user satisfaction. A series of experiments is surveyed to illustrate the gradual realization of getting non-experts to utilize non-textual query features through interface adjustments. Notably, the paper explores the use of the search system by government intelligence analysts, concluding that a variety of search methods are useful for news video retrieval and lead to improved satisfaction. This community, dominated by text search system expertise but still new to video and image search, performed better with and favored a system with image and concept query capabilities over an exclusive text-search system. The user study also found that sports topics mean nothing for this user community and tens of relevant shots collected into the answer set are considered enough to satisfy the information need. Lessons learned from these user interactions are reported, with recommendations on both interface improvements for video retrieval systems and enhancing the ecological validity of video retrieval interface evaluations.


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.

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Christel, M. G., and Conescu, R. M. Mining Novice User Activity with TRECVID Interactive Retrieval Tasks. In LNCS 4071: Proc. Image and Video Retrieval (CIVR) (Tempe, AZ, July 2006), Springer, Berlin, 2006, 21--30.
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Hollink, L., Nguyen, G. P., Koelma, D. C., Schreiber, A. T., and Worring, M. Assessing User Behaviour in News Video Retrieval. IEE Proc. Vision, Image, & Signal Processing 152(6), 2005, 911--918.
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National Institute of Standards and Technology, NIST TREC Video Retrieval Evaluation Online Proceedings, 2001--2006, http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.org.html.
 
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Shneiderman, B., Byrd, D., and Croft, W. B. Clarifying Search: A User-Interface Framework for Text Searches. D-Lib Magazine, 3, 1 (January 1997), http://www.dlib.org.
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Snoek, C., Worring, M., Koelma, D., and Smeulders, A. Learned Lexicon-Driven Interactive Video Retrieval. In LNCS 4071: Proc. Image and Video Retrieval (CIVR) (Tempe, AZ, July 2006), Springer, Berlin, 2006, 11--20.
 
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Snoek, C., Worring, M., Koelma, D., and Smeulders, A. A Learned Lexicon-Driven Paradigm for Interactive Video Retrieval. IEEE Trans. Multimedia 9(2), Feb. 2007, 280--292.
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Yan, R., and Hauptmann, A. G. Efficient Margin-Based Rank Learning Algorithms for Information Retrieval. In LNCS 4071: Proc. Image and Video Retrieval (CIVR) (Tempe, AZ, July 2006), Springer, Berlin, 2006, 113--122.
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Collaborative Colleagues:
Michael G. Christel: colleagues