Discrete online cross-modal hashing with consistency preservation

Online cross-modal hashing has attracted widespread attention with the rapid expansion of large-scale streaming data, which can reduce storage requirements and enhance efficiency for online cross-modal retrieval. However, despite promising progress, existing methods still suffer from defective accuracy in a way, primarily attributed to two issues: insufficient semantic information exploitation and mismatched training-retrieval process. To address these challenges, we propose a novel supervised hashing method with dual consistency preservation, called Discrete Online Cross-Modal Hashing (DOCMH). On the one hand, we design more informative continuous semantic labels and fine-grained similarity graphs to preserve semantic consistency across different streaming data chunks and modality representations. On the other hand, we propose an effective modality deviation calibration mechanism for preserving learning process consistency between the training and retrieval phases. Extensive experiments on three widely used benchmark datasets demonstrate the superior performance of the proposed DOCMH under various scenarios.