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The combination limit in multimedia retrieval
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Source 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
Authors
Rong Yan  Carnegie Mellon University, Pittsburgh, PA
Alexander G. Hauptmann  Carnegie Mellon University, Pittsburgh, PA
Sponsors
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
SIGCOMM: ACM Special Interest Group on Data Communication
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 4,   Downloads (12 Months): 16,   Citation Count: 9
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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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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.

CITED BY  9

Collaborative Colleagues:
Rong Yan: colleagues
Alexander G. Hauptmann: colleagues