Attributed hypergraph matching on a Riemannian manifold

If we consider a matching that preserves high-order relationships among points in the same set, we can int-roduce a hypergraph-matching technique to search for correspondence according to high-order feature values. While graph matching has been widely studied, there is limited research available reg...

Full description

Saved in:
Bibliographic Details
Published in:Machine vision and applications 2014-05, Vol.25 (4), p.823-844
Main Authors: Wang, J. M., Chen, S. W., Fuh, C. S.
Format: Article
Language:English
Subjects:
Applied sciences
Communications Engineering
Computer Science
Computer science
control theory
systems
Exact sciences and technology
Graph matching
Graph theory
Image Processing and Computer Vision
Information retrieval. Graph
Machine vision
Manifolds
Matching
Mathematical analysis
Missing data
Networks
Original Paper
Pattern Recognition
Polynomials
Preserves
Riemann manifold
Tensors
Theoretical computing
Vision systems
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:If we consider a matching that preserves high-order relationships among points in the same set, we can int-roduce a hypergraph-matching technique to search for correspondence according to high-order feature values. While graph matching has been widely studied, there is limited research available regarding hypergraph matching. In this paper, we formulate hypergraph matching in terms of tensors. Then, we reduce the hypergraph matching to a bipartite matching problem that can be solved in polynomial time. We then extend this hypergraph matching to attributed hypergraph matching using a combination of different attributes with different orders. We perform analyses that demonstrate that this method is robust when handling noisy or missing data and can achieve inexact graph matching. To the best of our knowledge, while attributed graph-matching and hypergraph-matching have been heavily researched, methods for attributed hypergraph matching have not been proposed before.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-014-0598-1