Discriminant Embedding for Local Image Descriptors

  title={Discriminant Embedding for Local Image Descriptors},
  author={Gang Hua and Matthew A. Brown and Simon A. J. Winder},
  journal={2007 IEEE 11th International Conference on Computer Vision},
Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and visual recognition. However, such descriptors are generally parameterised in very high dimensional spaces e.g. 128 dimensions in the case of SIFT. This limits the performance of feature matching techniques in terms of speed and scalability. Furthermore, these descriptors have traditionally been carefully hand crafted by manually tuning many parameters. In this paper, we tackle… CONTINUE READING
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