A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized deterministic annealing
IEEE Transactions on Neural Networks
A Lagrangian relaxation network for graph matching
IEEE Transactions on Neural Networks
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We present a practical implementation of a structural matching algorithm that uses the generalized deterministic annealing theory. The problem is formulated as follows: given a set of model points and object points, find a matching algorithm that brings the two sets of points into correspondence. An "energy" term represents the distance between the two sets of points. This energy has many local minima and the purpose is to escape from these local minima and to find the global minimum using the simulated annealing theory. We use a windowed implementation and a suitable definition of the energy function that reduces the computational effort of this annealing schedule without decreasing the solution quality.