Intra-class low-rank regularization for supervised and semi-supervised cross-modal retrieval

Unsupervised Deep Relative Neighbor Relationship Preserving Cross-Modal Hashing

The image-text cross-modal retrieval task, which aims to retrieve the relevant image from text and vice versa, is now attracting widespread attention. To quickly respond to the large-scale task, we propose an Unsupervised Deep Relative Neighbor Relationship Preserving Cross-Modal Hashing (DRNPH) to achieve cross-modal retrieval in the common Hamming space, which has the advantages of storage and efficiency. To fulfill the nearest neighbor search in the Hamming space, we demand to reconstruct both the original intra- and inter-modal neighbor matrix according to the binary feature vectors. Thus, we can compute the neighbor relationship among different modal samples directly based on the Hamming distances. Furthermore, the cross-modal pair-wise similarity preserving constraint requires the similar sample pair have an identical Hamming distance to the anchor. Therefore, the similar sample pairs own the same binary code, and they have minimal Hamming distances. Unfortunately, the pair-wise similarity preserving constraint may lead to an imbalanced code problem. Therefore, we propose the cross-modal triplet relative similarity preserving constraint, which demands the Hamming distances of similar pairs should be less than those of dissimilar pairs to distinguish the samples’ ranking orders in the retrieval results. Moreover, a large similarity marginal can boost the algorithm’s noise robustness. We conduct the cross-modal retrieval comparative experiments and ablation study on two public datasets, MIRFlickr and NUS-WIDE, respectively. The experimental results show that DRNPH outperforms the state-of-the-art approaches in various image-text retrieval scenarios, and all three proposed constraints are necessary and effective for boosting cross-modal retrieval performance.