In this paper we introduce the two-point correlation function as a measure
of interclass separability. We present a theoretical study of this statisti
c in a general M-dimensional feature space and propose a fast algorithm for
the efficient computation of it. We test the algorithm and illustrate the
properties of the statistic using test data in 1D and 2D feature spaces and
discuss the boundary effects of the feature space. We also present a discu
ssion of the limitations of the proposed statistic and apply it to the asse
ssment of inter-class separability in a texture segmentation context.