A Tensor-Based Algorithm for High-Order Graph Matching

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 multi-dimensional 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.

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Francis R. Bach, In-So Kweon, Jean Ponce, Olivier Duchenne
• Keywords: Computer Vision, CVPR 2009, Graph Matching, Hypergraph Matching