%0 Journal Article %A KANG Peipei %A LIN Zehang %A YANG Zhenguo %A ZHANG Zitong %A LIU Wenyin %T Semantic Preserving Hash for Cross-Modal Retrieval %D 2022 %R 10.3778/j.issn.1002-8331.2103-0325 %J Computer Engineering and Applications %P 149-155 %V 58 %N 21 %X Due to the low storage and high speed of hash representation, hash based cross-modal retrieval has aroused considerable attention. Most of the supervised cross-modal hashing methods learn semantic discriminant hash codes by regression or the graph constraint. However, this kind of methods ignore the semantic discrimination of hash functions, making the out-of-sample data unable to acquire semantic preserving hash codes, and limit the accuracy of cross-modal retrieval. In order to simultaneously learn semantic preserving hash codes and hash functions, this paper proposes the semantic preserving hash(SPH) for cross-modal retrieval. SPH introduces two hash functions that project data in cross-modal spaces into the common Hamming space. And to enhance the discrimination of both hash codes and hash functions, the semantic graph is brought in. Combining the theory of locality preserving, SPH fuses the hash codes learning and hash functions learning into one common framework and optimizes them together. Experiments on three public multimodal datasets show the effectiveness and superiority of SPH on the task of cross-modal retrieval. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2103-0325