Abstract
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 it can be efficiently combined with the dedicated state-of-the-art Lagrange 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 notably improve performance of these methods in terms of speed and quality of the obtained solutions. Hence, this combination results in a state-of-the-art solver for graph matching.
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URL
https://arxiv.org/abs/2101.12085
