Recent years have witnessed wide application of hashing for large-scale image retrieval.
However, most existing hashing methods are based on hand-crafted features which might …

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 …

Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-
scale image retrieval tasks. For most existing hashing methods, an image is first encoded as …

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 …

With the rapid growth of web images, hashing has received increasing interests in large
scale image retrieval. Research efforts have been devoted to learning compact binary codes …

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 …

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

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF,
OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of …

Hashing is one of the most popular and powerful approximate nearest neighbor search
techniques for large-scale image retrieval. Most traditional hashing methods first represent …