Visible to the public Double-bit quantization and weighting for nearest neighbor search

TitleDouble-bit quantization and weighting for nearest neighbor search
Publication TypeConference Paper
Year of Publication2017
AuthorsDeng, H., Xie, H., Ma, W., Mao, Z., Zhou, C.
Conference Name2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Keywordsbinary code, Binary codes, binary embedding, binary embedding method, DBQW, Double-bit quantization, double-bit quantization and weighting, Indexes, Measurement, Metrics, nearest neighbor search, NN search, pubcrawl, quantisation (signal), Quantization (signal), real-value signature conversion, search problems, weighted hamming distance

Binary embedding is an effective way for nearest neighbor (NN) search as binary code is storage efficient and fast to compute. It tries to convert real-value signatures into binary codes while preserving similarity of the original data. However, it greatly decreases the discriminability of original signatures due to the huge loss of information. In this paper, we propose a novel method double-bit quantization and weighting (DBQW) to solve the problem by mapping each dimension to double-bit binary code and assigning different weights according to their spatial relationship. The proposed method is applicable to a wide variety of embedding techniques, such as SH, PCA-ITQ and PCA-RR. Experimental comparisons on two datasets show that DBQW for NN search can achieve remarkable improvements in query accuracy compared to original binary embedding methods.

Citation Keydeng_double-bit_2017