Efficient approximate nearest neighbor search by optimized residual vector quantization

Abstract

In this paper, an optimized residual vector quantization-based approach is presented for approximate nearest neighbor search. The main contributions are as follows. Built 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 and 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. Experimental results on SIFT and GIST datasets demonstrate that our approaches outperform the state-of-the-art methods in vector quantization and approximate nearest neighbor search.