Multi-Semantic Embedding Hashing for LargeScale Cross-Modal Retrieval

With 5G and IoT booming, explosive multimodal data growth challenges communication bandwidth and retrieval accuracy. Cross-modal hashing stands out in cross-modal retrieval tasks owing to low storage requirements and swift retrieval advantages of binary encoding. However, existing methods often rely on coarse-grained semantics and single supervision information, ignoring the impact of fine-grained semantics and joint supervision. To handle the above dilemmas, this paper proposes Multi-Semantic Embedding Hashing (MSEH) for large-scale cross-modal retrieval. First, modalityspecific representations are learned to explore modality-private semantic information. Then, fine-grained semantic information is mined through multimodal latent space learning and semantic center learning. Finally, multiple semantics are embedded, and hash code learning is jointly supervised. Extensive empirical tests on three classic datasets demonstrate MSEH outperforms 6 state-of-the-art methods.