An Overview of Pattern Recognition Methods on Texture Classification

Pattern recognition (PR) is a subject that deals with the identification or interpretation of the pattern in an image. It aims to extract information about the image to classify its contents. Inputs are in the form of digitized binary valued 2D images or textures containing the pattern to be classified. The analysis and recognition of the patterns such as images and textures are becoming more and more complex and multiform. This is because in general the patterns to be analyzed are shifting from simple to complex, and because the patterns of heavy variations and with heavy noise have to be treated. Therefore it is proposed to develop sophisticated strategies of pattern analysis to cope with these difficulties.
In this paper three basic approaches of pattern recognition are analyzed: statistical pattern recognition, structural pattern recognition and neural pattern recognition. In the statistical approach the recognition is based on the decision boundaries that are established in the feature space by statistical distribution of the patterns. In the structural (syntactic) approach each pattern class is defined by a structural description or representation. The recognition is performed according to the similarity of structures. This is based on the fact that the significant information is not only the features but also the relationships consisting among the features. In the neural network based approach the artificial neural networks are able to form complex decision regions for pattern recognition. The present work involves in the study of Pattern recognition methods on Texture Classifications.