One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D cluster variation method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy (epsilon(0)) and the interaction enthalpy (epsilon(1)). Two different initial topographies ("scale-free-like" and "extreme rich club-like") were each computationally brought to a CVM free energy minimum, for the case where the activation enthalpy was zero and different values were used for the interaction enthalpy. The results are: (1) the computational configuration variable results differ significantly from the analytically-predicted values well before epsilon(1) approaches the known divergence as epsilon(1) -> 0.881, (2) the range of potentially useful parameter values, favoring clustering of like-with-like units, is limited to the region where epsilon(0) < 3 and epsilon(1) < 0.25, and (3) the topographies in the systems that are brought to a free energy minimum show interesting visual features, such as extended "spider legs" connecting previously unconnected "islands," and as well as evolution of "peninsulas" in what were previously solid masses.
