Material Classification on Symmetric Positive Definite Manifolds

Abstract

This paper tackles the problem of categorizing materials and textures by exploiting the second order statistics. To this end, we introduce the Extrinsic Vector of Locally Aggregated Descriptors (E-VLAD), a method to combine local and structured descriptors into a unified vector representation where each local descriptor is a Covariance Descriptor (CovD). In doing so, we make use of an accelerated method of obtaining a visual codebook where each atom is itself a CovD. We will then introduce an efficient way of aggregating local CovDs into a vector representation. Our method could be understood as an extrinsic extension of the highly acclaimed method of Vector of Locally Aggregated Descriptors [17] (or VLAD) to CovDs. We will show that the proposed method is extremely powerful in classifying materials/ textures and can outperform complex machineries even with simple classifiers.