Improving Texture Recognition using Combined GLCM and Wavelet Features

Texture is an important perceptual property of images based on which image content can be characterized and searched for in a Content Based Search and Retrieval (CBSR) system. This paper investigates techniques for improving texture recognition accuracy by using a set of Wavelet Decomposition Matrices (WDM) in conjunction with Grey Level Co-occurrence Matrices (GLCM). The texture image is decomposed at 3 levels using a 2D Haar Wavelet and a coefficient computed from the decomposition matrices is combined with features derived from a set of normalized symmetrical GLCMs computed along four directions, to provide improved accuracy. The proposed scheme is tested on a set of 13 textures derived from the Brodatz database and is seen to provide accuracies of the order of 90%. General Terms Pattern Recognition, Computer Vision, Wavelet Representation