Texture Classification by ICA

ICA (Independent Component Analysis) is a mathematical tool traditionally employed for source separation. In this paper, we test its ability for texture analysis, in order to provide a new texture classification method. From the multitude of the existing algorithms, we have chosen FastICA, a version based on the forth order statistics of the analyzed signal. By FastICA, a texture is decomposed in a weighted sum of components with a rather high degree of independence. Each component is further described by means of its negentropy, which is a measure of the nongaussianity. We show experimentally, that the three most nongaussian components of each analyzed texture are able to cluster the test samples.