Multiview Cross-Media Hashing with Semantic Consistency
Cross-media hashing is used to handle both cross-media representation and indexing simultaneously. Most existing methods attempt to bridge the semantic gap by maximizing the correlation of heterogeneous instances describing the same information object. Although these methods guarantee that such instances are close in the commonly shared space, instances describing different objects but the same category may be scattered. We propose a new cross-media hashing scheme, multiview cross-media hashing with semantic consistency (MCMHSC), to address this problem. By fully exploiting the semantic correlation and complementary information among objects, MCMHSC builds discriminative hashing codes. Experiments on two public benchmark datasets show that our proposed scheme achieves comparable or better performance compared to state-of-the-art methods in terms of accuracy and time complexity.