  Label information is widely used in hashing methods because of its
effectiveness of improving the precision. The existing hashing methods always
use two different projections to represent the mutual regression between hash
codes and class labels. In contrast to the existing methods, we propose a novel
learning-based hashing method termed stable supervised discrete hashing with
mutual linear regression (S2DHMLR) in this study, where only one stable
projection is used to describe the linear correlation between hash codes and
corresponding labels. To the best of our knowledge, this strategy has not been
used for hashing previously. In addition, we further use a boosting strategy to
improve the final performance of the proposed method without adding extra
constraints and with little extra expenditure in terms of time and space.
Extensive experiments conducted on three image benchmarks demonstrate the
superior performance of the proposed method.
