Self-organizing maps for texture classification

A further investigation of our intelligent machine vision system for pattern recognition and texture image classification is discussed in this paper. A data set of 335 texture images is to be classified into several classes, based on their texture similarities, while no a priori human vision expert knowledge about the classes is available. Self-Organizing Maps (SOM) neural networks are used for solving the classification problem. Although, in some of the experiments, a supervised texture analysis method is considered for comparison purposes. Four major experiments are conducted: in the first one, classifiers are trained using all the extracted features without any statistical pre-processing; in the second simulation, the available features are normalized before being fed to a classifier; in the third experiment, the trained classifiers use linear transformations of the original features, received after pre-processing with Principal Component Analysis (PCA); and in the last one, transforms of the features obtained after applying Linear Discriminant Analysis (LDA) are used. During the simulation, each test is performed 50 times using the proposed algorithm. Results from the employed unsupervised learning, after training, testing and validation of the SOMs, are analysed and critically compared with results from other authors.