Projected Residual Vector Quantization for ANN Search

In this research, we propose Projected Residual Vector Quantization (PRVQ) to deal with the problem of large-scale approximate nearest neighbor (ANN) search in a high-dimensional space. A lot of quantization-based ANN search algorithms have been proposed in the past few years. However, most of the existing methods discard the projection errors generated in the dimension reduction process, which inevitably decreases the search accuracy. In view of that, the authors propose a method of projected residual vector quantization for ANN search that considers the projection errors in the quantization process. They also design three simple and effective optimization strategies to improve the performance of the PRVQ algorithm. The authors have integrated the proposed PRVQ algorithm into a mobile landmark recognition system to prove its effectiveness.