Graph matching is an important problem in computer
vision. It is used in 2D and 3D object matching and recognition.
Despite its importance, there is little literature on
learning the parameters that control the graph matching
problem, even though learning is important for improving
the matching rate, as shown by this and other work. In this
paper we show for the first time how to perform parameter
learning in an unsupervised fashion, that is when no
correct correspondences between graphs are given during
training. We show empirically that unsupervised learning
is comparable in efficiency and quality with the supervised
one, while avoiding the tedious manual labeling of ground
truth correspondences. We also verify experimentally that
this learning method can improve the performance of several
state-of-the art graph matching algorithms.