Quantized Kernel Learning for Feature Matching

@inproceedings{Qin2014QuantizedKL,
  title={Quantized Kernel Learning for Feature Matching},
  author={Danfeng Qin and Xuanli Chen and Matthieu Guillaumin and Luc Van Gool},
  booktitle={NIPS},
  year={2014}
}
Matching local visual features is a crucial problem in computer vision and its accuracy greatly depends on the choice of similarity measure. As it is generally very difficult to design by hand a similarity or a kernel perfectly adapted to the data of interest, learning it automatically with as few assumptions as possible is preferable. However, available techniques for kernel learning suffer from several limitations, such as restrictive parametrization or scalability. In this paper, we… CONTINUE READING

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