— This paper introduces a product quantization based approach for approximate nearest neighbor search. The idea is to decomposes the space into a Cartesian product of low dimensi...
Fractal image encoding is a computationally intensive method of compression due to its need to find the best match between image sub-blocks by repeatedly searching a large virtual...
Handling large amounts of data, such as large image databases, requires the use of approximate nearest neighbor search techniques. Recently, Hamming embedding methods such as spec...
Hashing based Approximate Nearest Neighbor (ANN) search has attracted much attention due to its fast query time and drastically reduced storage. However, most of the hashing metho...
Given a set of n points in d-dimensional Euclidean space, S ⊂ Ed , and a query point q ∈ Ed , we wish to determine the nearest neighbor of q, that is, the point of S whose Euc...
This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perception areas such as computer v...
Ting Liu, Andrew W. Moore, Alexander G. Gray, Ke Y...
We present the first spatially adaptive data structure that answers approximate nearest neighbor (ANN) queries to points that reside in a geometric space of any constant dimension...
Jonathan Derryberry, Don Sheehy, Maverick Woo, Dan...
Abstract. We consider approximate nearest neighbor searching in metric spaces of constant doubling dimension. More formally, we are given a set S of n points and an error bound &g...
Sunil Arya, David M. Mount, Antoine Vigneron, Jian...
This paper proposes new methods to answer approximate nearest neighbor queries on a set of n points in d-dimensional Euclidean space. For any xed constant d, a data structure with...
We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under ÐÔ norm, based on Ôstable distributions. Our scheme improves the running...
Mayur Datar, Nicole Immorlica, Piotr Indyk, Vahab ...