Discriminative correlation hashing for supervised cross-modal retrieval

Due to their storage and calculational efficiency, hashing techniques have been used for cross-modal retrieval on large-scale multi-modal data. Cross-modal hashing methods retrieve relevant items of one modality for the query of the other modality by mapping heterogeneous data of different modalities into a common Hamming space, where the binary codes are generated. However, the existing cross-modal hashing methods pay little attention to the discriminative property of the binary codes. In this paper, we propose a novel supervised cross-modal hashing method, named Discriminative Correlation Hashing (DCH), which integrates discriminative property into the hashing learning procedure. DCH introduces the Linear Discriminant Analysis (LDA) to preserve the discriminative property of textual modality and transfers it to the corresponding image modality by the learned unified binary code, thus making data in the common Hamming space much more discriminative. Extensive experimental results demonstrate that DCH outperforms state-of-the-art cross-modal hashing methods.