Many-to-many matching in l1 norm

The problem of object recognition can be formulated as matching feature sets of different objects. Segmentation errors and scale difference result in many-to-many matching of feature sets, rather than one-to-one. This paper extends a previous algorithm on many-to-many graph matching. The proposed work represents graphs, which correspond to objects, isomorphically in the geometric space under the l₁ norm. Empirical evaluation of the algorithm on an extensive set of recognition trails, including a comparison with the previous approach, demonstrates the efficacy of the overall framework.