IEEE Trans Pattern Anal Mach Intell - A Tensor-Based Algorithm for High-Order Graph Matching.

Tópicos

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Resumo

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.

Resumo Limpo

paper address problem establish correspond two set visual featur use higherord constraint instead unari pairwis one use classic method concret correspond hypergraph match problem formul maxim multilinear object function permut featur function defin tensor repres affin featur tupl maxim use general spectral techniqu relax problem first solv multidimension power method solut project onto closest assign matrix propos approach implement compar stateoftheart algorithm synthet real data

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