Fuzzy connectives based optimal mapping of homomorphic ARG matching onto self-organising Hopfield network

Attributes used in object recognition can be considered fuzzy variables as they are generally noisy, unreliable and ambiguous. In this paper, the authors employ fuzzy information aggregation operators to optimally map the attributed relational graph (ARG) matching problem onto the self-organising Hopfield network. The computation of the parameters used in the information aggregation operators is formulated as a constraint optimization problem and solved using the gradient projection based learning algorithm. The mapping scheme ensures that the problem is optimally mapped for every model. Experimental results clearly show the usefulness and necessity of the learning scheme