  Hashing plays an important role in information retrieval, due to its low
storage and high speed of processing. Among the techniques available in the
literature, multi-modal hashing, which can encode heterogeneous multi-modal
features into compact hash codes, has received particular attention. Most of
the existing multi-modal hashing methods adopt the fixed weighting factors to
fuse multiple modalities for any query data, which cannot capture the variation
of different queries. Besides, many methods introduce hyper-parameters to
balance many regularization terms that make the optimization harder. Meanwhile,
it is time-consuming and labor-intensive to set proper parameter values. The
limitations may significantly hinder their promotion in real applications. In
this paper, we propose a simple, yet effective method that is inspired by the
Hadamard matrix. The proposed method captures the multi-modal feature
information in an adaptive manner and preserves the discriminative semantic
information in the hash codes. Our framework is flexible and involves a very
few hyper-parameters. Extensive experimental results show the method is
effective and achieves superior performance compared to state-of-the-art
algorithms.
