With the rapid development of information storage and networking technologies, quintillion
bytes of data are generated every day from social networks, business transactions, sensors …

Recently, learning based hashing techniques have attracted broad research interests due to
the resulting efficient storage and retrieval of images, videos, documents, etc. However, a …

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 …

With benefits of low storage costs and high query speeds, hashing methods are widely
researched for efficiently retrieving large-scale data, which commonly contains multiple …

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose
distances to a query item are the smallest from a large database. Various methods have …

Extracting informative image features and learning effective approximate hashing functions
are two crucial steps in image retrieval. Conventional methods often study these two steps …

The most striking successes in image retrieval using deep hashing have mostly involved
discriminative models, which require labels. In this paper, we use binary generative …

The past decade has been characterized by the growing volumes of data due to the
widespread use of the Internet of Things (IoT) applications, which introduced many …

This paper proposes a new hashing framework to conduct similarity search via the following
steps: first, employing linear clustering methods to obtain a set of representative data points …

Nowadays, due to the exponential growth of user generated images and videos, there is an
increasing interest in learning-based hashing methods. In computer vision, the hash …