Abstract
A novel approach for feature extraction of texture images based on NonSubsampled Contourlet Transform (NSCT) was proposed. The coefficients in different scales and different directions were obtained by textural image decomposition using NSCT. Then the means and variances of theses coefficients were extracted to be the feature vectors,which could greatly reduce the number of feature dimension. Back Propagation (BP) neural network was adopted to implement automatic classification of texture images through training and simulation. Compared with wavelet package transform and the improved Local Binary Pattern (LBP) texture descriptor,this approach can achieve better result.
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