Next Article in Journal
A High-Throughput Enzyme Assay for Organophosphate Residues in Milk
Previous Article in Journal
Ultrasonic Sensitivity of Strain-Insensitive Fiber Bragg Grating Sensors and Evaluation of Ultrasound-Induced Strain
Sensors 2010, 10(12), 11259-11273; doi:10.3390/s101211259

Approximate Nearest Neighbor Search by Residual Vector Quantization

2,*  and 1
1 Digital Engineering & Simulation Research Center, Huazhong University of Science and Technology, Wuhan 430074, China 2 School of Computer Science & Technology, Huazhong University of Science and Technology, No.1037 Luoyu Road, Wuhan 430074, China
* Author to whom correspondence should be addressed.
Received: 9 October 2010 / Revised: 20 November 2010 / Accepted: 7 December 2010 / Published: 8 December 2010
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [172 KB, uploaded 21 June 2014]   |   Browse Figures


A recently proposed product quantization method is efficient for large scale approximate nearest neighbor search, however, its performance on unstructured vectors is limited. This paper introduces residual vector quantization based approaches that are appropriate for unstructured vectors. Database vectors are quantized by residual vector quantizer. The reproductions are represented by short codes composed of their quantization indices. Euclidean distance between query vector and database vector is approximated by asymmetric distance, i.e., the distance between the query vector and the reproduction of the database vector. An efficient exhaustive search approach is proposed by fast computing the asymmetric distance. A straight forward non-exhaustive search approach is proposed for large scale search. Our approaches are compared to two state-of-the-art methods, spectral hashing and product quantization, on both structured and unstructured datasets. Results show that our approaches obtain the best results in terms of the trade-off between search quality and memory usage.
Keywords: approximate nearest neighbor search; high-dimensional indexing; residual vector quantization approximate nearest neighbor search; high-dimensional indexing; residual vector quantization
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote |
MDPI and ACS Style

Chen, Y.; Guan, T.; Wang, C. Approximate Nearest Neighbor Search by Residual Vector Quantization. Sensors 2010, 10, 11259-11273.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert