  Nowadays the measure between heterogeneous data is still an open problem for
cross-modal retrieval. The core of cross-modal retrieval is how to measure the
similarity between different types of data. Many approaches have been developed
to solve the problem. As one of the mainstream, approaches based on subspace
learning pay attention to learning a common subspace where the similarity among
multi-modal data can be measured directly. However, many of the existing
approaches only focus on learning a latent subspace. They ignore the full use
of discriminative information so that the semantically structural information
is not well preserved. Therefore satisfactory results can not be achieved as
expected. We in this paper propose a discriminative supervised subspace
learning for cross-modal retrieval(DS2L), to make full use of discriminative
information and better preserve the semantically structural information.
Specifically, we first construct a shared semantic graph to preserve the
semantic structure within each modality. Subsequently, the Hilbert-Schmidt
Independence Criterion(HSIC) is introduced to preserve the consistence between
feature-similarity and semantic-similarity of samples. Thirdly, we introduce a
similarity preservation term, thus our model can compensate for the
shortcomings of insufficient use of discriminative data and better preserve the
semantically structural information within each modality. The experimental
results obtained on three well-known benchmark datasets demonstrate the
effectiveness and competitiveness of the proposed method against the compared
classic subspace learning approaches.
