Multimedia Systems

, Volume 23, Issue 2, pp 169–181

Optimized residual vector quantization for efficient approximate nearest neighbor search

  • Liefu Ai
  • Junqing Yu
  • Zebin Wu
  • Yunfeng He
  • Tao Guan
Regular Paper

DOI: 10.1007/s00530-015-0470-9

Cite this article as:
Ai, L., Yu, J., Wu, Z. et al. Multimedia Systems (2017) 23: 169. doi:10.1007/s00530-015-0470-9

Abstract

In this paper, an optimized residual vector quantization-based approach is presented for improving the quality of vector quantization and approximate nearest neighbor search. The main contributions are as follows. Based on residual vector quantization (RVQ), a joint optimization process called enhanced RVQ (ERVQ) is introduced. Each stage codebook is iteratively optimized by the others aiming at minimizing the overall quantization errors. Thus, an input vector is approximated by its quantization outputs more accurately. Consequently, the precision of approximate nearest neighbor search is improved. To efficiently find nearest centroids when quantizing vectors, a non-linear vector quantization method is proposed. The vectors are embedded into 2-dimensional space where the lower bounds of Euclidean distances between the vectors and centroids are calculated. The lower bound is used to filter non-nearest centroids for the purpose of reducing computational costs. ERVQ is noticeably optimized in terms of time efficiency on quantizing vectors when combining with this method. To evaluate the accuracy that vectors are approximated by their quantization outputs, an ERVQ-based exhaustive method for approximate nearest neighbor search is implemented. Experimental results on three datasets demonstrate that our approaches outperform the state-of-the-art methods over vector quantization and approximate nearest neighbor search.

Keywords

Approximate nearest neighbor search Vector quantization Codebook optimization Filtration 

Funding information

Funder NameGrant NumberFunding Note
National Natural Science Foundation of China
  • 61173114, 61202300 and 61272202

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.School of Computer and InformationAnqing Normal UniversityAnqingChina
  3. 3.Center of Network and ComputationHuazhong University of Science and TechnologyWuhanChina

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