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.<<ETX>>

[1]  Dinesh P. Mital,et al.  Programming Hopfield network for object recognition , 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[2]  Wei-Chung Lin,et al.  A hierarchical multiple-view approach to three-dimensional object recognition , 1991, IEEE Trans. Neural Networks.

[3]  N. M. Nasrabadi,et al.  Object recognition based on graph matching implemented by a Hopfield-style neural network , 1989, International 1989 Joint Conference on Neural Networks.

[4]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[5]  J. J. Hopfield,et al.  “Neural” computation of decisions in optimization problems , 1985, Biological Cybernetics.

[6]  Nasser M. Nasrabadi,et al.  Hopfield network for stereo vision correspondence , 1992, IEEE Trans. Neural Networks.