Graph matching via sequential monte carlo
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Efficient and scalable 4th-order match propagation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Robust point pattern matching based on spectral context
Pattern Recognition
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This paper addresses the problem of establishing correspondences between two sets of visual features using higher order constraints instead of the unary or pairwise ones used in classical methods. Concretely, the corresponding hypergraph matching problem is formulated as the maximization of a multilinear objective function over all permutations of the features. This function is defined by a tensor representing the affinity between feature tuples. It is maximized using a generalization of spectral techniques where a relaxed problem is first solved by a multidimensional power method and the solution is then projected onto the closest assignment matrix. The proposed approach has been implemented, and it is compared to state-of-the-art algorithms on both synthetic and real data.