Classification of off-diagonal points in a co-occurrence matrix

The classification of the off-diagonal points within a typical grey level co-occurrence matrix (GLCM) is discussed through the application of an intuitive nearest peak and a boundary rule method. Both approaches are applied to a synthetic image consisting of five regions with varying amounts of added random noise and also to an image containing three Brodatz textures of different standard deviation. The two approaches correctly identify the majority of the internal region pixels. However, the nearest peak method is shown to produce serious misclassifications at the region boundaries in the form of bands of additional regions. The boundary rule method does not show this characteristic. The overall classification accuracy and the k_hat statistic were used to test the performance of each technique.