Feature matching is used to build correspondences between features in the model and test images. As the extension of graph matching, hypergraph matching is able to encode rich invariance between feature tuples and improve matching accuracy. Different from many existing algorithms based on maximizing the matching score between correspondences, our approach formulates hypergraph matching as a non-cooperative multi-player game and obtains matches by extracting the evolutionary stable strategies (ESS). While this approach generates a high matching accuracy, the number of matches is usually small and it involves a large computation load to obtain more matches. To solve this problem, we extract multiple ESS clusters instead of one single ESS group, thereby transforming hypergraph matching of features to hy-pergraph clustering of candidate matches. By extracting an appropriate number of clusters, we increase the number of matches efficiently, and improve the matching accuracy by imposing the one-to-one con-straint. In experiments with three real datasets, our algorithm is shown to generate a large number of matches efficiently. It also shows significant advantage in matching accuracy in comparison with some other hypergraph matching algorithms. (c) 2022 Elsevier Ltd. All rights reserved.

Hypergraph matching via game-theoretic hypergraph clustering

Pelillo, M;
2022

Abstract

Feature matching is used to build correspondences between features in the model and test images. As the extension of graph matching, hypergraph matching is able to encode rich invariance between feature tuples and improve matching accuracy. Different from many existing algorithms based on maximizing the matching score between correspondences, our approach formulates hypergraph matching as a non-cooperative multi-player game and obtains matches by extracting the evolutionary stable strategies (ESS). While this approach generates a high matching accuracy, the number of matches is usually small and it involves a large computation load to obtain more matches. To solve this problem, we extract multiple ESS clusters instead of one single ESS group, thereby transforming hypergraph matching of features to hy-pergraph clustering of candidate matches. By extracting an appropriate number of clusters, we increase the number of matches efficiently, and improve the matching accuracy by imposing the one-to-one con-straint. In experiments with three real datasets, our algorithm is shown to generate a large number of matches efficiently. It also shows significant advantage in matching accuracy in comparison with some other hypergraph matching algorithms. (c) 2022 Elsevier Ltd. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5004661
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