Gaussian similarity preserving for cross-modal hashing

Cross-modal hashing (CMH) has gained considerable attention due to its low storage and fast query speed. Most existing CMH research pursues the binary codes of each modality by preserving a similarity matrix which is computationally expensive. In this paper, we propose a Gaussian similarity preserving method for cross-modal hashing. By using the semantic transformation, our model can avoid computing the similarity matrix explicitly. We further employ the sequential learning approach to reduce the quantization error. Experimental results on three benchmark datasets clearly show that our proposed method can outperform the state-of-the-art methods. CO 2022 Elsevier B.V. All rights reserved.