LOCAL FREQUENCY FEATURES FOR TEXTURE CLASSIFICATION

We propose a texture feature extraction method which allows very small training sets, and due to its very local nature is suitable for classification of small textured structures. The method is based on detection of local extremas along a set of direction vectors in the spatial image representation. The local extremas are interpreted as identifying local frequencies, and used to design features corresponding to the basal frequency characteristics amplitude and wavelength. The good texture discrimination ability of these features is demonstrated on two images, and in a quantitative comparison with features from Gabor filtered images and co-occurrence matrix features. Of the three images used in the quantitative comparison, the local frequency features performed best on two of them. We conclude that the new method is a promising feature extraction method for non-stochastic textures, and that it should be further explored.