A Structural Matching Algorithm Using Generalized Deterministic Annealing
TL;DR: A practical implementation of a structural matching algorithm that uses the generalized deterministic annealing theory and a suitable definition of the energy function that reduces the computational effort of this annealed schedule without decreasing the solution quality is presented.
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Abstract: 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.
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References
A Lagrangian relaxation network for graph matching
Anand Rangarajan,Eric Mjolsness +1 more
- 01 Nov 1996
TL;DR: A Lagrangian relaxation network for graph matching is presented, with the application of a fixpoint preserving algebraic transformation to both the distance measure and self-amplification terms, and performs minimization and maximization on the permutation matrix variables.
57
A Lagrangian relaxation network for graph matching
Anand Rangarajan,Eric Mjolsness +1 more
- 27 Jun 1994
TL;DR: In this paper, the authors adopt a deterministic annealing approach which is similar to a Lagrangian decomposition approach in that the row and column constraints of the permutation matrix are satisfied separately and Lagrange multipliers are used to equate the two "solutions".
49
Generalized deterministic annealing
Scott T. Acton,Alan C. Bovik +1 more
TL;DR: The empirical data taken in conjunction with the formal analytical results suggest that GDA enjoys significant performance advantages relative to current methods for combinatorial optimization.
A graduated assignment algorithm for graph matching
Steven Gold,Anand Rangarajan +1 more
TL;DR: A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise, and not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching.