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SPLX-Perm: A Novel Permutation-Based Representation for Approximate Metric Search

  • Lucia VadicamoEmail author
  • Richard Connor
  • Fabrizio Falchi
  • Claudio Gennaro
  • Fausto Rabitti
Conference paper
First Online:
  • 455 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11807)

Abstract

Many approaches for approximate metric search rely on a permutation-based representation of the original data objects. The main advantage of transforming metric objects into permutations is that the latter can be efficiently indexed and searched using data structures such as inverted-files and prefix trees. Typically, the permutation is obtained by ordering the identifiers of a set of pivots according to their distances to the object to be represented. In this paper, we present a novel approach to transform metric objects into permutations. It uses the object-pivot distances in combination with a metric transformation, called n-Simplex projection. The resulting permutation-based representation, named SPLX-Perm, is suitable only for the large class of metric space satisfying the n-point property. We tested the proposed approach on two benchmarks for similarity search. Our preliminary results are encouraging and open new perspectives for further investigations on the use of the n-Simplex projection for supporting permutation-based indexing.

Keywords

Approximate metric search Permutation-based indexing Metric embedding n-point property n-Simplex projection 
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Notes

Acknowledgements

This work was partially supported by VISECH ARCO-CNR, CUP B56J17001330004, the AI4EU project, funded by the EC (H2020 - Contract n. 825619), and the Short-Term-Mobility (STM) program of the CNR.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lucia Vadicamo
    • 1
    Email author
  • Richard Connor
    • 2
  • Fabrizio Falchi
    • 1
  • Claudio Gennaro
    • 1
  • Fausto Rabitti
    • 1
  1. 1.Institute of Information Science and Technologies (ISTI)PisaItaly
  2. 2.Division of Mathematics and Computing ScienceUniversity of StirlingStirlingScotland

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