Accumulative Quantization for Approximate Nearest Neighbor Search

Author:

Ai Liefu12ORCID,Tao Yong1,Cheng Hongjun1,Wang Yuanzhi1,Xie Shaoguo3,Liu Deyang12,Zheng Xin12

Affiliation:

1. School of Computer and Information, Anqing Normal University, Anqing 246133, China

2. University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133, China

3. School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246133, China

Abstract

To further improve the approximate nearest neighbor (ANN) search performance, an accumulative quantization (AQ) is proposed and applied to effective ANN search. It approximates a vector with the accumulation of several centroids, each of which is selected from a different codebook. To provide accurate approximation for an input vector, an iterative optimization is designed when training codebooks for improving their approximation power. Besides, another optimization is introduced into offline vector quantization procedure for the purpose of minimizing overall quantization errors. A hypersphere-based filtration mechanism is designed when performing AQ-based exhaustive ANN search to reduce the number of candidates put into sorting, thus yielding better search time efficiency. For a query vector, a self-centered hypersphere is constructed, so that those vectors not lying in the hypersphere are filtered out. Experimental results on public datasets demonstrate that hypersphere-based filtration can improve ANN search time efficiency with no weakening of search accuracy; besides, the proposed AQ is superior to the state of the art on ANN search accuracy.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

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