Incremental Multi-graph Matching via Diversity and Randomness Based Graph Clustering

Multi-graph matching refers to finding correspondences across graphs, which are traditionally solved by matching all the graphs in a single batch. However in real-world applications, graphs are often collected incrementally, rather than once for all. In this paper, we present an incremental multi-graph matching approach, which deals with the arriving graph utilizing the previous matching results under the global consistency constraint. When a new graph arrives, rather than re-optimizing over all graphs, we propose to partition graphs into subsets with certain topological structure and conduct optimization within each subset. The partitioning procedure is guided by the diversity within partitions and randomness over iterations, and we present an interpretation showing why these two factors are essential. The final matching results are calculated over all subsets via an intersection graph. Extensive experimental results on synthetic and real image datasets show that our algorithm notably improves the efficiency without sacrificing the accuracy.