Texture feature extraction based on wavelet transform

The 2-D lifting-based DWT 9/7 wavelet filter is used here, without additional computations, giving lifting-based architectures a significant advantage over convolutional filter band-based architectures. This paper describes the texture classification using (i) the known texture images are decomposed using 9/7 wavelet. Then, mean and standard deviation of approximation and detail sub-bands of 3- level decomposed images are calculated. They are wavelet statistical features (WSFs) (ii) In order to improve the correct classification rate further, it is proposed to find co-occurrence matrix features for original image, approximation and detail sub-bands of 1-level 9/7 wavelet decomposed images. The various co-occurrence features such as contrast, energy, entropy and homogeneity are calculated from the co-occurrence matrix. These are wavelet co-occurrence features (WCFs). (iii) At last, the combination of WSFs and WCFs (feature vector) are used to classify images.