Graduated assignment graph matching

A new technique, termed softassign, is applied to weighted graph matching. Softassign, which has emerged from the recurrent neural network/statistical physics framework, enforces two-way (assignment) constraints without the use of penalty terms in the energy functions. The softassign technique is compared to softmax (Potts glass) dynamics. Within the statistical physics framework, softmax with a penalty term has been a widely used method for enforcing the two-way constraints common to many combinatorial optimization problems. The benchmarks present evidence that softassign has clear advantages in accuracy, speed, and algorithmic simplicity over softmax with a penalty term in this weighted graph matching problem.