ASYMMETRIC SCALABLE CROSS-MODAL HASHING

Cross-modal hashing is a practical approach to solving the problem of large-scale multimedia retrieval. However, there are still specific issues that the current methods cannot solve, such as how to construct binary codes rather than relax them to continuity effectively and how to prevent $n \times n$ problem. This paper proposes a novel Asymmetric Scalable Cross-Modal Hashing (ASCMH) to address these issues. It learns a common latent space from the kernelized features of different modalities. It then transforms the similarity matrix optimization to a distance-distance difference minimization problem with the help of semantic labels and common latent space. Additionally, we use an orthogonal constraint of label information to construct hash codes necessary for search accuracy. Extensive experiments on three benchmark datasets show that our ASCMH outperforms the SOTA cross-modal hashing methods.