Similarity search finds application in database systems handling complex data such as
images or videos, which are typically represented by high-dimensional features and require …

Recently, there has been a pressing need to manage high-dimensional vector data in data
science and AI applications. This trend is fueled by the proliferation of unstructured data and …

Nearest neighbor search is a problem of finding the data points from the database such that
the distances from them to the query point are the smallest. Learning to hash is one of the …

The in-memory algorithms for approximate nearest neighbor search (ANNS) have achieved
great success for fast high-recall search, but are extremely expensive when handling very …

Nearest neighbor search is a fundamental and essential operation in applications from
many domains, such as databases, machine learning, multimedia, and computer vision …

A new data structure for efficient similarity search in very large datasets of high-dimensional
vectors is introduced. This structure called the inverted multi-index generalizes the inverted …

In computer vision there has been increasing interest in learning hashing codes whose
Hamming distance approximates the data similarity. The hashing functions play roles in both …

Existing billion-scale nearest neighbor search systems have mostly been compared on a
single dataset of a billion of SIFT vectors, where systems based on the Inverted Multi-Index …

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 …

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