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Evaluation of Hashing Methods Performance on Binary Feature Descriptors

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Image Processing and Communications Challenges 9 (IP&C 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 681))

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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.

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Notes

  1. 1.

    See: https://get.google.com/tango/.

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Acknowledgment

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

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Authors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Jacek Komorowski & Tomasz Trzciński

Authors
  1. Jacek Komorowski
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  2. Tomasz Trzciński
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Correspondence to Jacek Komorowski .

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Editors and Affiliations

  1. University of Science and Technology, UTP, Bydgoszcz, Poland

    Michał Choraś

  2. University of Science and Technology, UTP, Bydgoszcz, Poland

    Ryszard S. Choraś

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Komorowski, J., Trzciński, T. (2018). Evaluation of Hashing Methods Performance on Binary Feature Descriptors. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications Challenges 9. IP&C 2017. Advances in Intelligent Systems and Computing, vol 681. Springer, Cham. https://doi.org/10.1007/978-3-319-68720-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-68720-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68719-3

  • Online ISBN: 978-3-319-68720-9

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