Transform Coding for Fast Approximate Nearest Neighbor Search in High Dimensions

We examine the problem of large scale nearest neighbor search in high dimensional spaces and propose a new approach based on the close relationship between nearest neighbor search and that of signal representation and quantization. Our contribution is a very simple and efficient quantization technique using transform coding and product quantization. We demonstrate its effectiveness in several settings, including large-scale retrieval, nearest neighbor classification, feature matching, and similarity search based on the bag-of-words representation. Through experiments on standard data sets we show it is competitive with state-of-the-art methods, with greater speed, simplicity, and generality. The resulting compact representation can be the basis for more elaborate hierarchical search structures for sub-linear approximate search. However, we demonstrate that optimized linear search using the quantized representation is extremely fast and trivially parallelizable on modern computer arc...

• Type: Journal
• Year: 2010
• Where: CVPR
• Authors: Jonathan Brandt
• Keywords: Computer Vision, CVPR 2010, Efficient Quantization Technique, Nearest Neighbor Search, Scale Nearest Neighbor