A Randomized Heuristic for Scene Recognition by Graph Matching

We propose a new strategy for solving the non-bijective graph matching problem in model-based pattern recognition. The search for the best correspondence between a model and an over-segmented image is formulated as a combinatorial optimization problem, defined by the relational attributed graphs representing the model and the image where recognition has to be performed, together with the node and edge similarities between them. A randomized construction algorithm is proposed to build feasible solutions to the problem. Two neighborhood structures and a local search procedure for solution improvement are also proposed. Computational results are presented and discussed, illustrating the effectiveness of the combined approach involving randomized construction and local search.