Product quantization with dual codebooks for approximate nearest neighbor search

Product quantization (PQ) is a powerful technique for approximate nearest neighbor (ANN) search. In this paper, to improve the accuracy of ANN search, we propose a new PQ-based method named product quantization with dual codebooks (DCPQ). Different from traditional PQ-based methods, we analyze quantization errors after learning the first PQ codebook, and then part of training vectors with larger quantization errors are found and selected to relearn a second PQ codebook. When encoding the database offline, all database vectors are firstly quantized using both of dual codebooks in each subspace, and the encoding mode of a database vector is determined after comparing the two quantization errors based on dual codebooks. Moreover, database vectors with the same encoding mode are grouped as a sub-database and can be more efficiently searched. Experimental results demonstrate that our proposed dual codebooks solution can achieve higher accuracy compared with the standard PQ and its variants. (C) 2020 Elsevier B.V. All rights reserved.