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.

翻译：我们为二次分配问题的近似算法作出了贡献,这种算法也称为图形匹配。受为多标签离散的Markov随机字段开发的聚变移动技术的成功启发,我们调查其对图形匹配的适用性。特别是,我们展示了如何有效地将聚变运动与最近显示计算机视觉和生物成像应用方面优异结果的专用最新双轨方法结合起来。正如我们对各种图表匹配数据集的经验评估所显示的那样,聚变在所获得解决方案的速度和质量方面大大改善了这些方法的绩效。我们的方法在竞争对手方面设置了新的最先进的技术,有显著的优势。