Shot retrieval plays a critical role in content-based video retrieval.Motivated by the theory of optimal matching in bipartite graph

we propose a novel approach based on the Kuhn-Munkres algorithm for shot retrieval.In contrast to existing algorithms

the proposed approach emphasizes one-to-one mapping among frames between two shots for effective similarity measure.A weighted bipartite graph is constructed to model the similarity between two shots:every vertex in a bipartite graph represents one frame in a shot

and the weight of every edge represents the similarity value for a pair of frames between two shots.Then Kuhn-Munkres algorithm is employed to compute the maximum weight of a constructed bipartite graph as the similarity value between two shots by guaranteeing the one-to-one mapping among frames.To improve the speed efficiency

we also propose two improved algorithms.Experimental results indicate that the proposed approach achieves superior performance than some existing methods.