Many-to-Many Matching in l(1) Norm

Abstract

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 l(1) 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.