Using covariance matrices for unsupervised texture segmentation

In this paper we propose an efficient unsupervised texture segmentation method. We introduce a texture extension of a state-of-the-art color segmentation algorithm. We show how to use covariance matrices of low level features for texture description. These features are efficiently calculated using integral images. Furthermore, a multi-scale extension allows to provide accurate texture segmentation results. An experimental evaluation on a synthetic texture database and images of the Berkeley image database demonstrate the improved performance of the algorithm.