Training discriminative classifiers for a large number of classes is a challenging problem due to increased ambiguities between classes. In order to better handle the ambiguities and to improve the scalability of classifiers to larger number of categories, we learn pairwise dissimilarity profiles (functions of spatial location) between categories and adapt them into nearest neighbor classification. We introduce a dissimilarity distance measure and linearly or nonlinearly combine it with direct distances. We illustrate and demonstrate the approach mainly in the context of appearance-based person recognition. © Springer-Verlag Berlin Heidelberg 2008.
CITATION STYLE
Lin, Z., & Davis, L. S. (2008). Learning pairwise dissimilarity profiles for appearance recognition in visual surveillance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5358 LNCS, pp. 23–34). https://doi.org/10.1007/978-3-540-89639-5_3
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