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 …

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 …

Hashing has emerged as a popular technique for fast nearest neighbor search in gigantic
databases. In particular, learning based hashing has received considerable attention due to …

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 …

Due to storage and search efficiency, hashing has become significantly prevalent for nearest
neighbor search. Particularly, deep hashing methods have greatly improved the search …

Hashing plays a pivotal role in nearest-neighbor searching for large-scale image retrieval.
Recently, deep learning-based hashing methods have achieved promising performance …

Nearest neighbor search is a fundamental problem in various research fields like machine
learning, data mining and pattern recognition. Recently, hashing-based approaches, for …

With explosive growth of data volume and ever-increasing diversity of data modalities, cross-
modal similarity search, which conducts nearest neighbor search across different modalities …

Hashing is becoming increasingly important in large-scale image retrieval for fast
approximate similarity search and efficient data storage. Many popular hashing methods aim …