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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.