Heterogeneous Pairwise-Semantic Enhancement Hashing for Large-Scale Cross-Modal Retrieval

Cross-modal hash learning has drawn widespread attention for large-scale multimodal retrieval because of its stability and efficiency in approximate similarity searches. However, most existing cross-modal hashing approaches employ discrete label-guided information to coarsely reflect intra- and intermodality correlations, making them less effective to measuring the semantic similarity of data with multiple modalities. In this paper, we propose a new heterogeneous pairwise-semantic enhancement hashing (HPsEH) for large-scale cross-modal retrieval by distilling higher-level pairwise-semantic similarity from supervision information. First, we adopt a supervised self-expression to learn a data-specific quantified semantic matrix, which uses real values to measure both the similarity and dissimilarity ranks of paired instances, such that the intrinsic semantics of the data can be well captured. Then, we fuse the label-based information and quantified semantic similarity to collaboratively learn the hash codes of multimodal data, such that both the intermodality consistency and modality-specific features can be simultaneously obtained during hash code learning. Moreover, we employ effective iterative optimization to address the discrete binary solution and massive pairwise matrix calculation, making the HPsEH scalable to large-scale datasets. Extensive experimental results on three widely used datasets demonstrate the superiority of our proposed HPsEH method over most state-of-the art approaches.