| The combination limit in multimedia retrieval |
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International Multimedia Conference
archive
Proceedings of the eleventh ACM international conference on Multimedia
table of contents
Berkeley, CA, USA
SESSION: Reception and posters
table of contents
Pages: 339 - 342
Year of Publication: 2003
ISBN:1-58113-722-2
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Downloads (6 Weeks): 0, Downloads (12 Months): 20, Citation Count: 7
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ABSTRACT
Combining search results from multimedia sources is crucial for dealing with heterogeneous multimedia data, particularly in multimedia retrieval where a final ranked list of items of interest is returned sorted by confidence or relevance. However, relatively little attention has been given to combination functions, especially their upper bound performance limits. This paper presents a theoretical framework for studying upper bounds for two types of combination functions. A general upper bound and two approximations are proposed for monotonic combination functions. We also studied the upper bounds for linear combination functions using a global optimization technique. Our experimental results show that the choice of combination functions has a considerable influence to retrieval performance.
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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Cynthia Dwork , Ravi Kumar , Moni Naor , D. Sivakumar, Rank aggregation methods for the Web, Proceedings of the 10th international conference on World Wide Web, p.613-622, May 01-05, 2001, Hong Kong, Hong Kong
[doi> 10.1145/371920.372165]
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D. Hush and B. Horne. Efficient algorithms for function approximation with piecewise linear sigmoidal networks. IEEE Trans. Neural Networks, 9(6), 1998.
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M. Naphade and et~al. Probabilistic multimedia objects (multijects): A novel approach to video indexing and retrieval in multimedia systems. In Proc. of ICIP, 1998.
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J. R. Smith and et~al. Interactive search fusion methods for video database retrieval. In IEEE Intl. Conf. on Image Processing, Barcelona, Spain, 2003.
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TREC2002. TREC2002 video track, http://www-nlpir.nist.gov/projects/t2002v/t2002v.html.
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R. Yan, A. Hauptmann, and R. Jin. Multimedia search with pseudo-relevance feedback. In International Conference on Image and Video Retrieval, Urbana, IL, USA, 2003.
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CITED BY 7
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Hanghang Tong , Jingrui He , Mingjing Li , Changshui Zhang , Wei-Ying Ma, Graph based multi-modality learning, Proceedings of the 13th annual ACM international conference on Multimedia, November 06-11, 2005, Hilton, Singapore
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Meng Wang , Xian-Sheng Hua , Xun Yuan , Yan Song , Li-Rong Dai, Optimizing multi-graph learning: towards a unified video annotation scheme, Proceedings of the 15th international conference on Multimedia, September 25-29, 2007, Augsburg, Germany
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Yi Wu , Edward Y. Chang , Kevin Chen-Chuan Chang , John R. Smith, Optimal multimodal fusion for multimedia data analysis, Proceedings of the 12th annual ACM international conference on Multimedia, October 10-16, 2004, New York, NY, USA
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