A novel approach for nonuniform list fusion

Wei-Qi Yan

doi:10.1117/12.708659

29 January 2007 A novel approach for nonuniform list fusion

Wei-Qi Yan

Proceedings Volume 6500, Document Recognition and Retrieval XIV; 65000N (2007) https://doi.org/10.1117/12.708659
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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.

Citation Download Citation

Wei-Qi Yan "A novel approach for nonuniform list fusion", Proc. SPIE 6500, Document Recognition and Retrieval XIV, 65000N (29 January 2007); https://doi.org/10.1117/12.708659

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