Similarity metrics for categorization: From monolithic to category specific

@article{Babenko2009SimilarityMF,
  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},
  year={2009},
  pages={293-300}
}
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
Highly Cited
This paper has 69 citations. REVIEW CITATIONS

8 Figures & Tables

Topics

Statistics

01020201020112012201320142015201620172018
Citations per Year

70 Citations

Semantic Scholar estimates that this publication has 70 citations based on the available data.

See our FAQ for additional information.