  Hash representation learning of multi-view heterogeneous data is the key to
improving the accuracy of multimedia retrieval. However, existing methods
utilize local similarity and fall short of deeply fusing the multi-view
features, resulting in poor retrieval accuracy. Current methods only use local
similarity to train their model. These methods ignore global similarity.
Furthermore, most recent works fuse the multi-view features via a weighted sum
or concatenation. We contend that these fusion methods are insufficient for
capturing the interaction between various views. We present a novel Central
Similarity Multi-View Hashing (CSMVH) method to address the mentioned problems.
Central similarity learning is used for solving the local similarity problem,
which can utilize the global similarity between the hash center and samples. We
present copious empirical data demonstrating the superiority of gate-based
fusion over conventional approaches. On the MS COCO and NUS-WIDE, the proposed
CSMVH performs better than the state-of-the-art methods by a large margin (up
to 11.41% mean Average Precision (mAP) improvement).
