  We contribute to approximate algorithms for the quadratic assignment problem
also known as graph matching. Inspired by the success of the fusion moves
technique developed for multilabel discrete Markov random fields, we
investigate its applicability to graph matching. In particular, we show how
fusion moves can be efficiently combined with the dedicated state-of-the-art
dual methods that have recently shown superior results in computer vision and
bio-imaging applications. As our empirical evaluation on a wide variety of
graph matching datasets suggests, fusion moves significantly improve
performance of these methods in terms of speed and quality of the obtained
solutions. Our method sets a new state-of-the-art with a notable margin with
respect to its competitors.
