  Hashing has been widely applied to large-scale multimedia retrieval due to
the storage and retrieval efficiency. Cross-modal hashing enables efficient
retrieval from database of one modality in response to a query of another
modality. Existing work on cross-modal hashing assumes heterogeneous
relationship across modalities for hash function learning. In this paper, we
relax the strong assumption by only requiring such heterogeneous relationship
in an auxiliary dataset different from the query/database domain. We craft a
hybrid deep architecture to simultaneously learn the cross-modal correlation
from the auxiliary dataset, and align the dataset distributions between the
auxiliary dataset and the query/database domain, which generates transitive
hash codes for heterogeneous multimedia retrieval. Extensive experiments
exhibit that the proposed approach yields state of the art multimedia retrieval
performance on public datasets, i.e. NUS-WIDE, ImageNet-YahooQA.
