Distributable Consistent Multi-Graph Matching
Nan Hu, Boris Thibert, Leonidas Guibas · arXiv (Cornell University) · 2016
In this paper we propose an optimization-based framework to multiple graph matching. The framework takes as input maps computed between pairs of graphs, and outputs maps that 1) are consistent among all pairs of graphs, and 2) preserve edge connectivity between pairs of graphs. The central idea of our approach is to divide the input graph into overlapping sub-graphs and enforce consistency among sub-graphs. This leads to a distributed formulation, which is scalable to large-scale datasets. We also present an equivalence condition between this decoupled scheme and the original scheme. Experiments on both synthetic and real-world datasets show that our framework is competent against state-of-the-art global optimization-based techniques.