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...

Full description

Saved in:
Bibliographic Details
Published in:2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 1980 - 1987
Main Authors: Duchenne, Olivier, Bach, Francis, Kweon, Inso, Ponce, Jean
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2009
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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