Methods are as follows: training by using the less part of the identification sample, the recognition rate is calculated by the increase of the certain step size and t the constraint between weight. The recognition rate as the Z axis, two weight values respectively as X, Y axis, the resulting points can be connected in a straight line in the 3 dimensional coordinate system, by solving the highest recognition rate, the optimal weights can be obtained. Through simulation experiments can be known, the optimal weights based on adaptive method are better in the recognition rate, weights obtained by adaptive computation of a few samples, suitable for parallel recognition calculation, can effectively improve the recognition rate of infrared images.
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Yalu Wu, Ruilong Li, Yi Xu, Liping Wang, "Infrared image recognition based on structure sparse and atomic sparse parallel," Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130J (14 December 2015);