When using Back Propagation (BP) neural network to recognize the spatial target, the high dimensional input features induce the complexity of the network structure and the poor performance of the recognition. In this paper, a new recognition method based on Spectral Regression (SR) feature dimension reduction and BP neural network is proposed for the above difficulties. Firstly, the HOG features are extracted from the spatial object, and then the feature dimensions are reduced by SR. Finally, the BP classifier is used to train the data. Experimental results show that the proposed method is better than the traditional dimension reduction methods such as PCA, KPCA, LPP, KLPP in dimension reduction and recognition, which can juggle real-time and accuracy, thus improving the recognition performance.
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WU Zhanjun, NIU Min, XU Bing, NIU Yanxiong,GENG Tianqi, ZHANG Fan, MAN Da . Research on Recognition Method Based on Spectral Regression and Back Propagation Neural Network. JEIT, 2016, 38(4): 978-984.
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