Nondeterministic kinetics associated with self-similarity processes with applications to autonomous fractal pattern clustering

Feature based pattern coding problem is considered within the framework of fractal collage theory. For this purpose, stochastic features are extracted to visualize pattern partitioning by not-yet-identified self-similarity process then aggregated following newly defined nondeterministic pattern kinetics. By reformulating 2D Newton potential in terms of the Hausdorff distance, feature points are associated with fixed points of contraction mappings to be identified. Feature clusters are successively expanded to minimize geometric inconsistency and complexity generation. The kinetic clustering scheme is verified through simulation studies. Simulation results demonstrate that the nondeterministic kinetics generates well ordered field of attractive forces for generating clusters of feature points consistent with mapping set to be identified.