Information theoretic clustering of large structural modelbases

A hierarchically structured approach to organizing large structural model bases using an information theoretic criterion is presented. Objects (patterns) are modeled in the form of random parametric structural descriptions (RPSDs), an extension of the parametric structural description graph-theoretic formalism. Hierarchically clustering the RPSDs reduces the computational work to O(log N). The node pointers allow a mapping between the observation and a stored representation at one level, and the mapping to all potential models at all subsequent levels is reduced to mere tests, eliminating the exponential search for the best interprimitive mapping function for each stored candidate pattern.<<ETX>>