Asymmetric and Discrete Self-Representation Enhancement Hashing for Cross-Domain Retrieval

Due to the characteristics of low storage requirement and high retrieval efficiency, hashing-based retrieval has shown its great potential and has been widely applied for information retrieval. However, retrieval tasks in real-world applications are usually required to handle the data from various domains, leading to the unsatisfactory performances of existing hashing-based methods, as most of them assuming that the retrieval pool and the querying set are similar. Most of the existing works overlooked the self-representation that containing the modality-specific semantic information, in the cross-modal data. To cope with the challenges mentioned above, this paper proposes an asymmetric and discrete self-representation enhancement hashing (ADSEH) for cross-domain retrieval. Specifically, ADSEH aligns the mathematical distribution with domain adaptation for cross-domain data, by exploiting the correlation of minimizing the distribution mismatch to reduce the heterogeneous semantic gaps. Then, ADSEH learns the self-representation which is embedded into the generated hash codes, for enhancing the semantic relevance, improving the quality of hash codes, and boosting the generalization ability of ADSEH. Finally, the heterogeneous semantic gaps are further reduced by the log-likelihood similarity preserving for the cross-domain data. Experimental results demonstrate that ADSEH can outperform some SOTA baseline methods on four widely used datasets.