  Hashing techniques have been applied broadly in retrieval tasks due to their
low storage requirements and high speed of processing. Many hashing methods
based on a single view have been extensively studied for information retrieval.
However, the representation capacity of a single view is insufficient and some
discriminative information is not captured, which results in limited
improvement. In this paper, we employ multiple views to represent images and
texts for enriching the feature information. Our framework exploits the
complementary information among multiple views to better learn the
discriminative compact hash codes. A discrete hashing learning framework that
jointly performs classifier learning and subspace learning is proposed to
complete multiple search tasks simultaneously. Our framework includes two
stages, namely a kernelization process and a quantization process.
Kernelization aims to find a common subspace where multi-view features can be
fused. The quantization stage is designed to learn discriminative unified
hashing codes. Extensive experiments are performed on single-label datasets
(WiKi and MMED) and multi-label datasets (MIRFlickr and NUS-WIDE) and the
experimental results indicate the superiority of our method compared with the
state-of-the-art methods.
