Perceptual texture retrieval using spatial distributions of textons (SDoT)

It has been shown that the spatial information of local image characteristics is important to human perception and computational features. Inspired by these studies, we propose a set of new computational texture features based on the spatial distributions of textons (SDoT). First, gradient magnitude and gradient direction spectra are computed from a texture image. Second, the multiple gradient spectra simultaneous autoregressive (MGSSAR) models are estimated for each image. Both model coefficients and the variance of the model estimation error jointly construct a local feature space. Third, &-means is used to learn textons from the local features. All textons learned from a texture database are combined into a dictionary. Fourth, vector quantization is utilized to map a texture from the local feature space into the texton space. Finally, an aura matrix is computed from the texton map of each texture in order to encode the spatial distributions of the textons. The results of a perceptual texture retrieval experiment show that the proposed feature set performs more consistently with human observers than 56 existing feature sets. We attribute this to the fact that the proposed feature set encodes the spatial information of textons.