For many computer vision and machine learning problems, large training sets are key for
good performance. However, the most computationally expensive part of many computer …

This paper presents a novel compact coding approach, composite quantization, for
approximate nearest neighbor search. The idea is to use the composition of several …

Recently, hashing based Approximate Nearest Neighbor (ANN) techniques have been
attracting lots of attention in computer vision. The data-dependent hashing methods, eg …

The k-NN graph has played a central role in increasingly popular data-driven techniques for
various learning and vision tasks; yet, finding an efficient and effective way to construct k-NN …

Product quantization-based approaches are effective to encode high-dimensional data
points for approximate nearest neighbor search. The space is decomposed into a Cartesian …

Many binary code embedding schemes have been actively studied recently, since they can
provide efficient similarity search, and compact data representations suitable for handling …

Modern data is characterized by its ever-increasing volume and complexity, particularly
when data instances belong to many categories simultaneously. This learning paradigm is …

Scalable image search based on visual similarity has been an active topic of research in
recent years. State-of-the-art solutions often use hashing methods to embed high …

There are increasing volumes of spatio-temporal data from various sources such as sensors,
social networks and urban environments. Analysis of such data requires flexible exploration …

In this paper, we address the approximate nearest neighbor (ANN) search problem over
large scale visual descriptors. We investigate a simple but very effective approach …