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 …

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 …

Recent vision and learning studies show that learning compact hash codes can facilitate
massive data processing with significantly reduced storage and computation. Particularly …

Image hashing is a principled approximate nearest neighbor approach to find similar items
to a query in a large collection of images. Hashing aims to learn a binary-output function that …

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 …

By leveraging semantic (label) information, supervised hashing has demonstrated better
accuracy than unsupervised hashing in many real applications. Because the hashing-code …

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 …

Hashing or binary code learning has been recognized to accomplish efficient near neighbor
search, and has thus attracted broad interests in recent retrieval, vision, and learning …

Unsupervised deep hash functions have not shown satisfactory improvements against the
shallow alternatives, and usually, require supervised pretraining to avoid getting stuck in …

Hashing-based similarity search is an important technique for large-scale query-by-example
image retrieval system, since it provides fast search with computation and memory …