Over the past decades, medical image analytics was greatly facilitated by the explosion of
digital imaging techniques, where huge amounts of medical images were produced with …

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 …

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 explosive growth in Big Data has attracted much attention in designing efficient indexing
and search methods recently. In many critical applications such as large-scale search and …

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 …

Hashing has been widely used for large-scale approximate nearest neighbor search
because of its storage and search efficiency. Recent work has found that deep supervised …

This paper addresses the problem of learning similarity-preserving binary codes for efficient
similarity search in large-scale image collections. We formulate this problem in terms of …

Motivated by large-scale multimedia applications we propose to learn mappings from high-
dimensional data to binary codes that preserve semantic similarity. Binary codes are well …

Product quantization (PQ) is an effective vector quantization method. A product quantizer
can generate an exponentially large codebook at very low memory/time cost. The essence …

Many binary code encoding schemes based on hashing have been actively studied
recently, since they can provide efficient similarity search, especially nearest neighbor …