  Recently, learning to hash has been widely studied for image retrieval thanks
to the computation and storage efficiency of binary codes. For most existing
learning to hash methods, sufficient training images are required and used to
learn precise hashing codes. However, in some real-world applications, there
are not always sufficient training images in the domain of interest. In
addition, some existing supervised approaches need a amount of labeled data,
which is an expensive process in term of time, label and human expertise. To
handle such problems, inspired by transfer learning, we propose a simple yet
effective unsupervised hashing method named Optimal Projection Guided Transfer
Hashing (GTH) where we borrow the images of other different but related domain
i.e., source domain to help learn precise hashing codes for the domain of
interest i.e., target domain. Besides, we propose to seek for the maximum
likelihood estimation (MLE) solution of the hashing functions of target and
source domains due to the domain gap. Furthermore,an alternating optimization
method is adopted to obtain the two projections of target and source domains
such that the domain hashing disparity is reduced gradually. Extensive
experiments on various benchmark databases verify that our method outperforms
many state-of-the-art learning to hash methods. The implementation details are
available at https://github.com/liuji93/GTH.
