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Evaluation of Hashing Methods Performance on Binary Feature Descriptors

  • Jacek KomorowskiEmail author
  • Tomasz Trzciński
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 681)

Abstract

In this paper we evaluate performance of data-dependent hashing methods on binary data. The goal is to find a hashing method that can effectively produce lower dimensional binary representation of 512-bit FREAK descriptors. A representative sample of recent unsupervised, semi-supervised and supervised hashing methods was experimentally evaluated on large datasets of labelled binary FREAK feature descriptors.

Keywords

Data-dependent hashing methods Binary feature descriptors 

Notes

Acknowledgment

This research was supported by Google’s Sponsor Research Agreement under the project “Efficient visual localization on mobile devices”.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Warsaw University of TechnologyWarsawPoland

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