Relaxation labeling using augmented lagrange-hopfield method

摘要

A novel relaxation labeling (RL) method is presented based on Augmented Lagrangian multipliers and the graded Hopfield neural network (ALH). In this method, an RL problem is converted into a constrained optimization problem and solved by using the augmented Lagrangian and Hopfield techniques. The ALH method yields results comparable to the best of the existing RL algorithms in terms of the optimized objective values, yet it is more suitable for analog neural implementation. Experimental results are presented.