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An efficient gradient computation approach to discriminative fusion optimization in semantic concept detection

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2 Author(s)
Chengyuan Ma ; Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA ; Chin-Hui Lee

In this paper, we propose an efficient gradient computation approach for discriminative fusion optimization in TRECVID high-level feature extraction. Numerical approximation was exploited in gradient calculation and model parameter update. The gradient of the performance measure was approximated by a sum of instance point-wise gradient instead of instance pair-wise gradient used in maximum figure-of-merit learning such that performance metrics like average precision can be optimized directly and efficiently on large training set. Experiments on the TRECVID 2005 high-level feature extraction test set showed that the proposed algorithm can improve the mean average precision from 0.254 of a state-of-the-art baseline system to 0.285.

Published in:

Pattern Recognition, 2008. ICPR 2008. 19th International Conference on

Date of Conference:

8-11 Dec. 2008

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