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
Link To Document :
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