• DocumentCode
    3748659
  • Title

    PQTable: Fast Exact Asymmetric Distance Neighbor Search for Product Quantization Using Hash Tables

  • Author

    Yusuke Matsui;Toshihiko Yamasaki;Kiyoharu Aizawa

  • Author_Institution
    Univ. of Tokyo, Tokyo, Japan
  • fYear
    2015
  • Firstpage
    1940
  • Lastpage
    1948
  • Abstract
    We propose the product quantization table (PQTable), a product quantization-based hash table that is fast and requires neither parameter tuning nor training steps. The PQTable produces exactly the same results as a linear PQ search, and is 102 to 105 times faster when tested on the SIFT1B data. In addition, although state-of-the-art performance can be achieved by previous inverted-indexing-based approaches, such methods do require manually designed parameter setting and much training, whereas our method is free from them. Therefore, PQTable offers a practical and useful solution for real-world problems.
  • Keywords
    "Artificial neural networks","Indexing","Tuning","Training","Quantization (signal)","Data structures"
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision (ICCV), 2015 IEEE International Conference on
  • Electronic_ISBN
    2380-7504
  • Type

    conf

  • DOI
    10.1109/ICCV.2015.225
  • Filename
    7410582