We propose a family of structured matrices to speed up orthogonal projections for high-
dimensional data commonly seen in computer vision applications. In this, a structured matrix …

Product quantization is an effective vector quantization approach to compactly encode high-
dimensional vectors for fast approximate nearest neighbor (ANN) search. The essence of …

Nearest neighbor search is a fundamental problem in computer vision and machine
learning. The straightforward solution, linear scan, is both computationally and memory …

This paper focuses on the problem of learning binary embeddings for efficient retrieval of
high-dimensional non-negative data. Such data typically arises in a large number of vision …

We present a simple vector quantizer that combines low distortion with fast search and apply
it to approximate nearest neighbor (ANN) search in high dimensional spaces. Leveraging …

Inspired by the close relation between nearest neighbor search and clustering in high-
dimensional spaces as well as the success of one helping to solve the other, we introduce a …

We propose an efficient algorithm to find the exact nearest neighbor based on the Euclidean
distance for large-scale computer vision problems. We embed data points nonlinearly onto a …

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 …

Many binary code embedding techniques have been proposed for large-scale approximate
nearest neighbor search in computer vision. Recently, product quantization that encodes the …

We present a new method for Product Quantization (PQ) based approximated nearest
neighbor search (ANN) in high dimensional spaces. Specifically, we first propose a …