Title: Asymmetric Deep Supervised Hashing,
Abstract: 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 hashing can significantly outperform non-deep supervised
hashing in many applications. However, most existing deep supervised hashing
methods adopt a symmetric strategy to learn one deep hash function for both
query points and database (retrieval) points. The training of these symmetric
deep supervised hashing methods is typically time-consuming, which makes them
hard to effectively utilize the supervised information for cases with
large-scale database. In this paper, we propose a novel deep supervised hashing
method, called asymmetric deep supervised hashing (ADSH), for large-scale
nearest neighbor search. ADSH treats the query points and database points in an
asymmetric way. More specifically, ADSH learns a deep hash function only for
query points, while the hash codes for database points are directly learned.
The training of ADSH is much more efficient than that of traditional symmetric
deep supervised hashing methods. Experiments show that ADSH can achieve
state-of-the-art performance in real applications.