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 a comparison with the previous approach, demonstrates the efficacy of the overall framework. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Demirci, M. F., & OsmanlIoǧlu, Y. (2009). Many-to-many matching under the l1 norm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5716 LNCS, pp. 787–796). https://doi.org/10.1007/978-3-642-04146-4_84
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