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Learning hierarchical similarity metrics
2012
2012 IEEE Conference on Computer Vision and Pattern Recognition
Categories in multi-class data are often part of an underlying semantic taxonomy. Recent work in object classification has found interesting ways to use this taxonomy structure to develop better recognition algorithms. Here we propose a novel framework to learn similarity metrics using the class taxonomy. We show that a nearest neighbor classifier using the learned metrics gets improved performance over the best discriminative methods. Moreover, by incorporating the taxonomy, our learned
doi:10.1109/cvpr.2012.6247938
dblp:conf/cvpr/VermaMSN12
fatcat:ktrw3nsvg5hlbjafk2pefd23ha