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Top1. Introduction
Texture classification is a process of determining the class of texture, from a set of known texture classes, to which a given image or sub-image belongs. Texture is an inherent property of an image and plays a vital role in distinguishing images. In the last few decades, remarkable progress has been made in texture classification but it is still considered as a challenging problem. Wide range of real-time applications of texture classification such as automated detection of defects and quality control of texture images, medical diagnosis, microscope images, postal address recognition and interpretation of maps, remote sensing and geological images has motivated the research community to develop robust and efficient techniques.
The performance of texture classification mainly depends on the choice of: (i) features to represent an image and (ii) classifier to learn a decision model. In literature, numerous feature extraction techniques have been proposed, which can be classified into four categories (Tuceryan & Jain, 1998; Materka & Strzelecki, 1998; Zhang & Tan, 2002): structural, statistical, model based and signal processing based techniques.
Structural techniques represent textures as being composed of simple primitive structures called “texels” (or textons or texture elements) and placement rules that govern their spatial arrangement. They provide a good symbolic description of an image and are most effective for representing regular textures. But extraction of texels is a complex process (Zhang & Tan, 2002). On the other hand, statistical techniques characterize the texture in terms of statistical properties that measures correlation among the grey levels of an image (Materka & Strzelecki, 1998). However, they do not explicitly consider the hierarchical structure of the texture. Moreover, the complexity of computing higher order statistical features increases with the increase in number of grey levels. Model-based techniques, construct a model of an image that can be used to describe as well as synthesize the texture (Zhang & Tan, 2002). The parameters of the model describe the basic texture properties. However, parameter estimation and the choice of suitable model are two basic problems in these techniques. In signal processing based techniques, features are constructed by convolving images with spatial or (and) frequency domain filters. These techniques perform well with both random and regular textures. But a key problem of these techniques is to choose or design appropriate filters.