Programming Hopfield network for relational homomorphism

We study the energy and compatibility function formulations for pattern recognition by homomorphic mapping of attributed relational graphs using the Hopfield network. A deterministic hypothesis initialization strategy is introduced and proven to be superior to the commonly used random initialization in many aspects. Further, a method to verify the validity of the hypotheses generated by the Hopfield network is also presented based on a compatible cluster formation procedure using binary compatibility measures. The compatible cluster formation method allows multiple hypotheses to be evaluated simultaneously and the best to be chosen. The performance of the homomorphic algorithm is evaluated using silhouette images.<>