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Efficient Discriminative Projections for Compact Binary Descriptors

  • Tomasz Trzcinski
  • Vincent Lepetit
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

Binary descriptors of image patches are increasingly popular given that they require less storage and enable faster processing. This, however, comes at a price of lower recognition performances. To boost these performances, we project the image patches to a more discriminative subspace, and threshold their coordinates to build our binary descriptor. However, applying complex projections to the patches is slow, which negates some of the advantages of binary descriptors. Hence, our key idea is to learn the discriminative projections so that they can be decomposed into a small number of simple filters for which the responses can be computed fast. We show that with as few as 32 bits per descriptor we outperform the state-of-the-art binary descriptors in terms of both accuracy and efficiency.

Keywords

Image Patch Stepwise Approach Integral Image Random Projection Stochastic Gradient Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tomasz Trzcinski
    • 1
  • Vincent Lepetit
    • 1
  1. 1.CVLabEPFLLausanneSwitzerland

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