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While the largest common subgraph (LCSG) between a query and a database of models can provide an elegant and intuitive measure of similarity for many applications, it is computationally expensive to compute. Recently developed algorithms for subgraph isomorphism detection take advantage of prior knowledge of a database of models to improve the speed of on-line matching. This paper presents a new algorithm based on similar principles to solve the largest common subgraph problem. The new algorithm signi"cantly reduces the computational complexity of detection of the LCSG between a known database of models, and a query given on-line.

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