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Performance Analysis of Graph-Based Methods for Exact and Approximate Similarity Search in Metric Spaces

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Similarity Search and Applications (SISAP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11223))

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Abstract

Similarity searches are widely used to retrieve complex data, such as images, videos, and georeferenced data. Recently, graph-based methods have emerged as a very efficient alternative for similarity retrieval. However, to the best of our knowledge, there are no previous works with experimental analysis on a comprehensive number of graph-based methods using the same search algorithm and execution environment. In this work, we survey the main graph-based types currently employed for similarity searches and present an experimental evaluation of the most representative graphs on a common platform. We evaluate the relative performance behavior of the tested graph-based methods with respect to the main construction and query parameters for a variety of real-world datasets. Our experimental results provide a quantitative view of the exact search compared to accurate setups for approximate search. These results reinforce the tradeoff between graph construction cost and search performance according to the construction and search parameters. With respect to the approximate methods, the Navigable Small World graph (NSW) presented the highest recall rates. Nevertheless, given a recall rate, there is no winner graph for query performance.

This work has been supported by the Brazilian agencies CAPES and CNPq.

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Authors and Affiliations

  1. University of Londrina, Londrina, PR, Brazil

    Larissa Capobianco Shimomura & Daniel S. Kaster

  2. Hitachi America Ltd, R&D, Santa Clara, CA, USA

    Marcos R. Vieira

Authors
  1. Larissa Capobianco Shimomura
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  2. Marcos R. Vieira
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  3. Daniel S. Kaster
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Correspondence to Larissa Capobianco Shimomura .

Editor information

Editors and Affiliations

  1. University of Geneva, Carouge, Switzerland

    Stéphane Marchand-Maillet

  2. Arizona State University, Tempe, AZ, USA

    Yasin N. Silva

  3. Center for Scientific Research and Higher Education, Ensenada, Mexico

    Edgar Chávez

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Shimomura, L.C., Vieira, M.R., Kaster, D.S. (2018). Performance Analysis of Graph-Based Methods for Exact and Approximate Similarity Search in Metric Spaces. In: Marchand-Maillet, S., Silva, Y., Chávez, E. (eds) Similarity Search and Applications. SISAP 2018. Lecture Notes in Computer Science(), vol 11223. Springer, Cham. https://doi.org/10.1007/978-3-030-02224-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-02224-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02223-5

  • Online ISBN: 978-3-030-02224-2

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