Features Extraction and Classification of Rice Paper Images Based on Wavelet Transform

Abstract

Based on wavelet transform for the classiflcation of image features, a new method for the classiflcation of image texture features is put forward. In our study, the images of rice paper have been acquired using a digital image system. The images of rice paper were decomposed respectively using Debaucheries and Gabor wavelet transforms. The subband of low frequency was selected to extract 11 kinds of classic characteristic value of Gray-level Co-occurrence Matrix (GLCM). Then the texture feature values were classifled by the Support Vector Machine (SVM). In order to evaluate the classiflcation accuracy, feature values of the original images and images processed by wavelet decomposition were sent into SVM individually. The classiflcation rate of rice paper texture images was only 84.1% using characteristic values of original images, but reached 93.0% by using Gabor wavelet. The overall results show that wavelet transform is a highly e‐cient method for paper classiflcation. In summary, the method of using wavelet decomposition for the recognition of rice image provides a new nondestructive and fast method for rice paper classiflcation.