  Hashing that projects data into binary codes has shown extraordinary talents
in cross-modal retrieval due to its low storage usage and high query speed.
Despite their empirical success on some scenarios, existing cross-modal hashing
methods usually fail to cross modality gap when fully-paired data with plenty
of labeled information is nonexistent. To circumvent this drawback, motivated
by the Divide-and-Conquer strategy, we propose Deep Manifold Hashing (DMH), a
novel method of dividing the problem of semi-paired unsupervised cross-modal
retrieval into three sub-problems and building one simple yet efficiency model
for each sub-problem. Specifically, the first model is constructed for
obtaining modality-invariant features by complementing semi-paired data based
on manifold learning, whereas the second model and the third model aim to learn
hash codes and hash functions respectively. Extensive experiments on three
benchmarks demonstrate the superiority of our DMH compared with the
state-of-the-art fully-paired and semi-paired unsupervised cross-modal hashing
methods.
