Fast algorithms for texture analysis using cooccurrence matrices

Texture analysis may be of great importance for the problem of image classification and recognition. Co-occurrence matrices are quite effective for discriminating different textures but have the disadvantage of a high computational cost. In the paper a fast algorithm for calculating parameters of co-occurrence matrices is presented. This method has been applied to the problem of classification and segmentation of artificial and natural scenes: the classification, based on co-occurrence matrix parameters, is implemented pixel-by-pixel by using supervised learning and maximum likelihood estimates. The problem of texture boundary recognition has also been considered and a classification scheme based on more than one window for each pixel is presented. Experimental results show the improvements of classification rates that can be achieved by using this method when compared to a single-window classification.