The Two-Point Correlation Function: A Measure of Interclass Separability

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In this paper we introduce the two-point correlation function as a measure of interclass separability. We present a theoretical study of this statistic 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 discussion of the limitations of the proposed statistic and apply it to the assessment of inter-class separability in a texture segmentation context.

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