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 …

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 …

Fast nearest neighbor search is becoming more and more crucial given the advent of large-
scale data in many computer vision applications. Hashing approaches provide both fast …

Approximate nearest neighbor (ANN) search has achieved great success in many tasks.
However, existing popular methods for ANN search, such as hashing and quantization …

We propose theoretical and empirical improvements for two-stage hashing methods. We first
provide a theoretical analysis on the quality of the binary codes and show that, under mild …

A very large volume of images is uploaded to the Internet daily. However, current hashing
methods for image retrieval are designed for static databases only. They fail to consider the …

Learning based hashing techniques have attracted broad research interests in the Big
Media research area. They aim to learn compact binary codes which can preserve semantic …

Hashing technique has become an effective method for information retrieval due to the fast
calculation of the Hamming distance. However, with the continuous growth of data coming …

Supervised hashing methods are widely-used for nearest neighbor search in computer
vision applications. Most state-of-the-art supervised hashing approaches employ batch …

Because of strict response-time constraints, efficiency of top-k recommendation is crucial for
real-world recommender systems. Locality sensitive hashing and index-based methods …