Abstract Image classification is one of the most important and basic problems in image processing, and designing an effective feature extraction method and a fast classifier with a high recognition rate are two key points in image classification. Polyharmonic random weights networks (P-RWNs) are proposed based on the random weights networks (RWNs) and the advantage of polynomial that it can approximate the part with small variation effectively. Based on the proposed P-RWNs, a method for image classification is presented by integrating fast discrete curvelet transform (FDCT) and discriminative locality alignment (DLA). In the proposed method, FDCT is used to extract features from images, then the dimensionalities of these features are reduced by DLA before the features are input to the proposed P-RWNs classifier for recognition. Experimental results show that the proposed image classification method achieves higher recognition rate and recognition speed.
ZHAO Jian-Wei,ZHOU Zheng-Hua,CAO Fei-Long. A Method for Image Classification Based on Polyharmonic Random Weights Networks and Curvelet Transform[J]. , 2014, 27(6): 509-516.
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2014, Vol. 27 
