  Most existing learning to hash methods assume that there are sufficient data,
either labeled or unlabeled, on the domain of interest (i.e., the target
domain) for training. However, this assumption cannot be satisfied in some
real-world applications. To address this data sparsity issue in hashing,
inspired by transfer learning, we propose a new framework named Transfer
Hashing with Privileged Information (THPI). Specifically, we extend the
standard learning to hash method, Iterative Quantization (ITQ), in a transfer
learning manner, namely ITQ+. In ITQ+, a new slack function is learned from
auxiliary data to approximate the quantization error in ITQ. We developed an
alternating optimization approach to solve the resultant optimization problem
for ITQ+. We further extend ITQ+ to LapITQ+ by utilizing the geometry structure
among the auxiliary data for learning more precise binary codes in the target
domain. Extensive experiments on several benchmark datasets verify the
effectiveness of our proposed approaches through comparisons with several
state-of-the-art baselines.
