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Unified Graph Matching in Euclidean Spaces
Julian McAuley, Teo de Campos and Tiberio Caetano
In: 23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 13-18 June 2010, San Francisco.
AbstractGraph matching is a classical problem in pattern recognition
with many applications, particularly when the graphs
are embedded in Euclidean spaces, as is often the case for
computer vision. There are several variants of the matching
problem, concerned with isometries, isomorphisms, homeomorphisms,
and node attributes; different approaches exist
for each variant. In this paper, we show how structured
estimation methods from machine learning can be used to
combine such variants into a single version of graph matching.
In this paradigm, the extent to which our dataset
reveals isometries, isomorphisms, homeomorphisms, and
other properties is automatically taken into account in the
learning process so that any such specific qualification of
graph matching loses meaning. We present experiments
with real computer vision data showing the leverage of this
unified formulation. [Edit]
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