K-centroids-based supervised classification of texture images using the SIRV modeling

Abstract

Natural texture images can exhibit high intra-class diversity due to the acquisition conditions. To reduce its impact on classification performances, the geometry of the cluster in the feature space should be considered. We introduce the Spherically Invariant Random Vector (SIRV) representation, which is based on scale-space decomposition, for the modeling of spatial dependencies characterizing the texture image. From the specific properties of the SIRV process, i.e. the independence between the two sub-processes of the compound model, we derive a centroid estimation scheme from a pseudo-distance i.e. the Jeffrey divergence. Next, a K-centroids based (K-CB) supervised classification algorithm is introduced to handle the intra-class variability of texture images in the feature space. A comparative study on various conventional texture databases is conducted and reveals the impact of the proposed classification algorithm. © 2013 Springer-Verlag.