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Proceedings Paper

A novel approach for nonuniform list fusion
Author(s): Wei-Qi Yan
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Paper Abstract

List fusion is a critical problem in information retrieval. The approach using uniform weights for list fusion ignores the correctness, importance and individuality of various detectors for a concrete application. In this paper, we propose a nonuniform and rational optimized paradigm for TRECVid list fusion, which is expected to loyally preserve the precision in the outcomes and reach the maximum Average Precision (A.P.). Therefore we exhaustively search for the corresponding parametric set for the best A.P. in the space spanned by the feature vectors. In order to accelerate the fusion procedure of the input score lists, we train our model using the training data set, and apply the learnt parameters to fuse those new vectors. We take the nonuniform rational blending functions into account, the advantage of using this fusion is that the problem of weights selection is converted to the issue of parameters selection in the space related to the nonuniform and rational functions. The high precision and multiple resolution, controllable and stable attributes of rational functions are helpful in parameters selection. Therefore, the space for fusion weights selection becomes large. The correctness of our proposal is compared and verified with the average and linear fusion results.

Paper Details

Date Published: 29 January 2007
PDF: 6 pages
Proc. SPIE 6500, Document Recognition and Retrieval XIV, 65000N (29 January 2007); doi: 10.1117/12.708659
Show Author Affiliations
Wei-Qi Yan, Univ. of California/Irvine (United States)


Published in SPIE Proceedings Vol. 6500:
Document Recognition and Retrieval XIV
Xiaofan Lin; Berrin A. Yanikoglu, Editor(s)

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