IEEE Trans Pattern Anal Mach Intell. 2017 Nov;39(11):2171-2185. doi: 10.1109/TPAMI.2016.2636200. Epub 2016 Dec 6.
We present an efficient matching method for generalized geometric graphs. Such graphs consist of vertices in space connected by curves and can represent many real world structures such as road networks in remote sensing, or vessel networks in medical imaging. Graph matching can be used for very fast and possibly multimodal registration of images of these structures. We formulate the matching problem as a single player game solved using Monte Carlo Tree Search, which automatically balances exploring new possible matches and extending existing matches. Our method can handle partial matches, topological differences, geometrical distortion, does not use appearance information and does not require an initial alignment. Moreover, our method is very efficient-it can match graphs with thousands of nodes, which is an order of magnitude better than the best competing method, and the matching only takes a few seconds.
我们提出了一种用于广义几何图的有效匹配方法。这种图由空间中的顶点通过曲线连接而成，可以表示许多真实世界的结构，例如遥感中的道路网络或医学成像中的血管网络。图匹配可用于非常快速且可能是多模态的这些结构的图像配准。我们将匹配问题表示为使用蒙特卡罗树搜索解决的单人游戏，该搜索自动平衡探索新的可能匹配和扩展现有匹配。我们的方法可以处理部分匹配、拓扑差异、几何变形，不使用外观信息，也不需要初始对齐。此外，我们的方法非常高效——它可以匹配具有数千个节点的图，这比最好的竞争方法要好一个数量级，并且匹配只需要几秒钟。
