Unsupervised texture discrimination based on rough fuzzy sets and parallel hierarchical clustering

The paper reports a texture separation algorithm to solve the problem of unsupervised boundary localization in textured images. The proposed algorithm is mainly characterized by the extraction of textural density gradients by a nonlinear multiple scale-space analysis of the image. Texture boundaries are extracted by segmenting the images resulting from a multiscale fuzzy gradient operation applied to detail images. The segmentation stage consists of a parallel hierarchical clustering algorithm, aimed at the minimization of a global cost functional taking into account region homogeneity and segmentation quality. Experiments and comparisons on Brodatz textures are reported.