  Hashing has recently sparked a great revolution in cross-modal retrieval
because of its low storage cost and high query speed. Recent cross-modal
hashing methods often learn unified or equal-length hash codes to represent the
multi-modal data and make them intuitively comparable. However, such unified or
equal-length hash representations could inherently sacrifice their
representation scalability because the data from different modalities may not
have one-to-one correspondence and could be encoded more efficiently by
different hash codes of unequal lengths. To mitigate these problems, this paper
exploits a related and relatively unexplored problem: encode the heterogeneous
data with varying hash lengths and generalize the cross-modal retrieval in
various challenging scenarios. To this end, a generalized and flexible
cross-modal hashing framework, termed Matrix Tri-Factorization Hashing (MTFH),
is proposed to work seamlessly in various settings including paired or unpaired
multi-modal data, and equal or varying hash length encoding scenarios. More
specifically, MTFH exploits an efficient objective function to flexibly learn
the modality-specific hash codes with different length settings, while
synchronously learning two semantic correlation matrices to semantically
correlate the different hash representations for heterogeneous data comparable.
As a result, the derived hash codes are more semantically meaningful for
various challenging cross-modal retrieval tasks. Extensive experiments
evaluated on public benchmark datasets highlight the superiority of MTFH under
various retrieval scenarios and show its competitive performance with the
state-of-the-arts.

Computer Vision and Pattern Recognition