To further improve the approximate nearest neighbor (ANN) search performance, an accumulative quantization (AQ) is proposed and applied to effective ANN search. It approximates a vector with the accumulation of several centroids, each of which is selected from a different codebook. To provide accurate approximation for an input vector, an iterative optimization is designed when training codebooks for improving their approximation power. Besides, another optimization is introduced into offline vector quantization procedure for the purpose of minimizing overall quantization errors. A hypersphere-based filtration mechanism is designed when performing AQ-based exhaustive ANN search to reduce the number of candidates put into sorting, thus yielding better search time efficiency. For a query vector, a self-centered hypersphere is constructed, so that those vectors not lying in the hypersphere are filtered out. Experimental results on public datasets demonstrate that hypersphere-based filtration can improve ANN search time efficiency with no weakening of search accuracy; besides, the proposed AQ is superior to the state of the art on ANN search accuracy.

Approximate nearest neighbor (ANN) search is fundamental for fast content-based image retrieval. While vector quantization is one key to performing an effective ANN search, in order to further improve ANN search accuracy, we propose an enhanced accumulative quantization (E-AQ). Based on our former work, we introduced the idea of the quarter point into accumulative quantization (AQ). Instead of finding the nearest centroid, the quarter vector was used to quantize the vector and was computed for each vector according to its nearest centroid and second nearest centroid. Then, the error produced through codebook training and vector quantization was reduced without increasing the number of centroids in each codebook. To evaluate the accuracy to which vectors were approximated by their quantization outputs, we realized an E-AQ-based exhaustive method for ANN search. Experimental results show that our approach gained up to 0.996 and 0.776 Recall@100 with eight size 256 codebooks on SIFT and GIST datasets, respectively, which is at least 1.6% and 4.9% higher than six other state-of-the-art methods. Moreover, based on the experimental results, E-AQ needs fewer codebooks while still providing the same ANN search accuracy.