Non-parametric similarity measures for unsupervised texture segmentation and image retrieval

In this paper we propose and examine non-parametric statistical tests to define similarity and homogeneity measures for textures. The statistical rests are applied to the coefficients of images filtered by a multi-scale Gabor filter bank. We will demonstrate that these similarity measures are useful for both, texture based image retrieval and for unsupervised texture segmentation, and hence offer an unified approach to these closely related tasks. We present results on Brodatz-like micro-textures and a collection of real-word images.