In this work, we introduce a new kind of spatial partition trees for efficient nearest-neighbor search. Our approach first identifies a set of useful data splitting directions, and then learns a codebook that can be used to encode such directions. We use the product-quantization idea in order to make the effective codebook large, the evaluation of scalar products between the query and the encoded splitting direction very fast, and the encoding itself compact. As a result, the proposed data srtucture (Product Split tree) achieves compact clustering of data points, while keeping the traversal very efficient. In the nearest-neighbor search experiments on high-dimensional data, product split trees achieved state-of-the-art performance, demonstrating better speed-accuracy tradeoff than other spatial partition trees.
Research areas
Published in
The IEEE Conference on Computer Vision and Pattern Recognition
25 Jul 2017