Image Classification Schemes Based on Statistical Moments of Wavelet and Sliced Radial Energy Distribution of DCT

Abstract

Texture recognition is used in various pattern recognition applications and texture classification that possess a characteristic appearance. This research paper aims to provide an improved scheme to provide enhanced classification decisions and to decrease processing time significantly. This research studied the discriminating characteristics of textures by extracting them from various texture images using discrete Wavelate transform (DWT) and discrete Cosine transform DCT. Two sets of features are proposed; the first set was extracted using the traditional DCT, while the second used DWT. The features from the Cosine domain are calculated using the radial distribution of spectra, while for those extracted from Wavelet was statistical distribution of various relative moments. Four types of Euclidean distance metrics were used for classification decision purposes. The considered method was applied on 475 classes of textures belonged to 32 sets from Salzburg Texture Image Database, each set holding 16 images per class, so the a total of 7600 images were tested. Each image was separated into three bands of color component (i.e., red, green, blue). Concepts of average and standard deviation were calculated to determine the inter/intra scatter analysis for each feature to find out the best discriminating features that can be used. The final result of DWT was 99.98 for the testing sets and 99.71 for the training sets, while the final result of DCT was 99.06 for the testing sets and 96.77 for the training sets.