Potts MFT neural networks for recognition of partially occluded shapes

In this paper, learning schemes are presented to optimally map the homomorphic graph matching problem onto the Potts mean field theory (MFT) neural networks. The computation of the weighting factors used in the compatibility measure equation is formulated as an optimization problem and solved using the quadratic programming procedure based learning algorithm. The formulation implicitly evaluates ambiguity, robustness and discriminatory power of the relational attributes chosen for graph matching and assigns weighting factors appropriately to these relational attributes. Further, the tolerance and steepness parameters are also learnt. These learning schemes also enable the construction of augmented weighted model attributed relational graphs (WARG). The proposed parameter learning schemes are employed to solve the silhouette objects recognition problem and the necessity for such learning schemes is also demonstrated