International Journal of Computer Applications (0975 – 8887) Volume 29– No.10, September 2011 41 Improving Texture Recognition using Combined GLCM and Wavelet Features Ranjan Parekh School of Education Technology Jadavpur University Kolkata, India ABSTRACT 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 Keywords Texture recognition, Grey Level Co-occurrence Matrix, Wavelet decomposition, Content Based Storage and Retrieval, Pattern Recognition. 1. INTRODUCTION In recent years the number of digital media repositories have grown exponentially, especially over the Web. Retrieving images from these repositories has, therefore, become an important research issue. With this ever expanding multimedia repository a fast and efficient search and retrieval system has become a necessity; because, without it an image repository becomes like a library of books without a catalogue - even though the information is present it is practically inaccessible to someone with specific search criteria. Texture is one of the important perceptual characteristics based on which image content can be characterized and searched. A popular texture recognition technique relates to Grey Level Co-occurrence Matrix (GLCM), proposed by Haralick (1979) and subsequently used in a number of research works. The present work investigates techniques for improving the texture recognition accuracy by using a set of Wavelet Decomposition Matrices (WDM); it also demonstrates further improvement when GLCM is used in conjunction with WDM. The organization of the paper is as follows: section 2 provides an overview of related work, section 3 outlines the proposed approach with discussions on overview, feature computation and classification schemes, section 4 provides details of the dataset and experimental results obtained and section 5 provides the overall conclusion and the scope for future research. 2. PREVIOUS WORK Texture refers to visual patterns or spatial arrangement of pixels that regional intensity or color alone cannot sufficiently describe. It is difficult to obtain a general mathematical model for various textures because of the large variation in their properties. A first example of the derivation of features using operators is the set of texture energy measures formulated by Laws in [1]. In [2] the authors derive texture operators from co- occurrence matrices. A simple operator for fast discrimination between textures and uniform regions has been proposed in [3]. Another method similar to Laws is described in [4]. Here a set of simple masks (vertical, horizontal, diagonal and anti- diagonal) are applied. Authors like Tamura [5] made an attempt at defining a set of visually relevant texture features. This includes coarseness, contrast, directionality, line-likeness, regularity, roughness. Fractal functions have received a great deal of attention in recent years. Pentland [6] reports a high degree of correlation between fractal dimensions and human estimates of roughness. Because of this correlation and the natural appearance of fractal generated textures, Pentland has proposed fractal functions as texture models. In [7] the authors describe a parallel algorithm for segmentation using simultaneous auto-regressive (SAR) random field models and multi-dimensional cluster analysis. In [8] the author proposes a two state Markov model to detect texture edges characterized by changes in first order statistics. Gabor filters have been used in several image analysis applications including texture classification and segmentation [9, 10]. Bovik et al [9] suggest the restriction of the choice of Gabor filters to those with isometric gaussians (aspect ratio one). In [11] the authors have used the one sided linear prediction (OSP) model, popularly known as auto-regressive (AR) model, to derive texture descriptors in terms of the prediction coefficients. 3. PROPOSED APPROACH 3.1 GLCM : An Overview GLCM (Grey Level Co-occurrence Matrix) introduced by Haralick [12] provides one of the most popular statistical methods in analysis of grey tones in an image. The matrix defines the probability that grey level i occurs at a distance d in direction θ from grey level j in the texture image. These probabilities create the co-occurrence matrix (, | , ) Mijd  . The symmetrical GLCM is formed by taking the transpose of the GLCM and adding it to the original GLCM. The normalized