Central Similarity Hashing for Efficient Image and Video Retrieval

Existing data-dependent hashing methods usually learn hash functions from the pairwise or triplet data relationships, which only capture the data similarity locally, and often suffer low learning efficiency and low collision rate. In this work, we propose a new\emph {global} similarity metric, termed as\emph {central similarity}, with which the hash codes for similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy. We principally formulate the computation of the proposed central similarity metric by introducing a new concept, ie\emph {hash center} that refers to a set of data points scattered in the Hamming space with sufficient mutual distance between each other. We then provide an efficient method to construct well separated hash centers by leveraging the Hadamard matrix and Bernoulli …