Multiscale image texture analysis in wavelet spaces

The paper describes a new method for texture feature extraction and analysis in images using wavelet transform (WT), KL-expansion and Kohonen maps. For this purpose, the authors first apply a global wavelet transform on the initial image. Due to the localization properties of the WT both in the spatial and in the frequency domain it is possible to describe the local texture features in the surroundings of any pixel by a set of respective wavelet coefficients. This is accomplished by a local traversal of the wavelet pyramid and finally results in the feature vector required. Since the localization is limited by Heisenberg's uncertainty principle one must approximate the single coefficients for each pixel by piecewise linear interpolation. Once the feature vector is derived from the WT, further steps in the analysis pipeline perform decorrelation, normalization and finally clustering and supervised classification. In contrast to many related wavelet-based approaches, that usually apply different WTs on every texture sample and classify based on means derived from the former, the present method especially accounts for many real world applications. In those cases there are not usually large coherent texture regions that allow separated treatment. Moreover the approach employs a global WT and then stresses the local properties of the basis functions to identify local areas of interest from the initial image, as for instance training areas. The authors illustrate the efficiency of the method by classifying different real world textures with LVQ classifiers.<>