Unsupervised texture segmentation using resonance algorithm for natural scenes

Abstract

Many texture segmentation methods in the literature assume that the changes of intensity can be ascribed to the texture themselves. However, the real-world images may contain wide-ranged gradations in intensity which have nothing to do with local texture, such as those caused by the environment illuminations and cameras. To overcome the problem, an unsupervised texture segmentation method is proposed in this paper. Emphasizing the spatial relations between the adjacent texture pixels, the algorithm begins from a set of seed pixels and the texture region is generated by including those similar pixels. To suppress the noise influence, special attention is paid to the similarity criterion. Furthermore, to meet the requirement of unsupervised segmentation, the threshold in the similarity checking is automatically determined via iteratively applying the algorithm. The experimental results on Brodatz texture images and real-world images are presented.