Deep Semantic Correlation Learning Based Hashing for Multimedia Cross-Modal Retrieval

For many large-scale multimedia datasets and web contents, the nearest neighbor search methods based on the hashing strategy for cross-modal retrieval have attracted considerable attention due to its fast query speed and low storage cost. Most existing hashing methods try to map different modalities to Hamming embedding in a supervised way where the semantic information comes from a large manual label matrix and each sample in different modalities is usually encoded by a sparse label vector. However, previous studies didn't address the semantic correlation learning challenges and couldn't make the best use of the prior semantic information. Therefore, they cannot preserve the accurate semantic similarities and often degrade the performance of hashing function learning. To fill this gap, we firstly proposed a novel Deep Semantic Correlation learning based Hashing framework (DSCH) that generates unified hash codes in an end-to-end deep learning architecture for cross-modal retrieval task. The major contribution in this work is to effectively automatically construct the semantic correlation between data representation and demonstrate how to utilize correlation information to generate hash codes for new samples. In particular, DSCH integrates latent semantic embedding with a unified hash embedding to strengthen the similarity information among multiple modalities. Furthermore, additional graph regularization is employed in our framework, to capture the correspondences from the inter-modal and intra-modal. Our model simultaneously learns the semantic correlation and the unified hash codes, which enhances the effectiveness of cross-modal retrieval task. Experimental results show the superior accuracy of our proposed approach to several state-of-the-art cross-modality methods on two large datasets.