Similarity metrics for categorization: From monolithic to category specific

  title={Similarity metrics for categorization: From monolithic to category specific},
  author={Boris Babenko and Steve Branson and Serge J. Belongie},
  journal={2009 IEEE 12th International Conference on Computer Vision},
Similarity metrics that are learned from labeled training data can be advantageous in terms of performance and/or efficiency. These learned metrics can then be used in conjunction with a nearest neighbor classifier, or can be plugged in as kernels to an SVM. For the task of categorization two scenarios have thus far been explored. The first is to train a single “monolithic” similarity metric that is then used for all examples. The other is to train a metric for each category in a 1-vs-all… CONTINUE READING
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