Spatio-chromatic image content descriptors and their analysis using extreme value theory

Abstract

We use the theory of group representations to construct very fast image descriptors that split the vector space of local RGB distributions into small group-invariant subspaces. These descriptors are group theoretical generalizations of the Fourier Transform and can be computed with algorithms similar to the FFT. Because of their computational efficiency they are especially suitable for retrieval, recognition and classification in very large image datasets. We also show that the statistical properties of these descriptors are governed by the principles of the Extreme Value Theory (EVT). This enables us to work directly with parametric probability distribution models, which offer a much lower dimensionality and higher resolution and flexibility than histogram representations. We explore the connection to EVT and analyse the characteristics of these descriptors from a probabilistic viewpoint with the help of large image databases. © 2011 Springer-Verlag.