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 multil...

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Veröffentlicht in:2009 IEEE Conference on Computer Vision and Pattern Recognition S. 1980 - 1987
Hauptverfasser: Duchenne, Olivier, Bach, Francis, Kweon, Inso, Ponce, Jean
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2009
Schlagworte:
Computer vision
Image classification
Image retrieval
Iterative algorithms
Object detection
Shape
Stereo vision
Tensile stress
Transmission line matrix methods
Vegetation mapping
ISBN:1424439922, 9781424439928
ISSN:1063-6919, 1063-6919
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Zusammenfassung: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.
ISBN:1424439922
9781424439928
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2009.5206619