Many-to-Many Matching under the 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, isometrically in the geometric space under the l(1) norm. Empirical evaluation of the algorithm on a set of recognition trails, including comparison with the previous approach, demonstrates the efficacy of the overall framework.