Approximate Nearest Neighbor Search by Residual Vector Quantization
AbstractA 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.
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Chen, Y.; Guan, T.; Wang, C. Approximate Nearest Neighbor Search by Residual Vector Quantization. Sensors 2010, 10, 11259-11273.
Chen Y, Guan T, Wang C. Approximate Nearest Neighbor Search by Residual Vector Quantization. Sensors. 2010; 10(12):11259-11273.Chicago/Turabian Style
Chen, Yongjian; Guan, Tao; Wang, Cheng. 2010. "Approximate Nearest Neighbor Search by Residual Vector Quantization." Sensors 10, no. 12: 11259-11273.