Method of subspace cross-modal retrieval based on hypergraph ranking

Based on the research of subspace cross-modal retrieval method based on high-level semantic correlation, a subspace cross-modal retrieval method based on hypergraph ranking is proposed. The method is mainly divided into two parts: subspace mapping and hypergraph sorting. In the subspace mapping part, the high-order semantic correlation and sparse structural constraints of multi-modal data are considered. The hypergraph sorting part uses hypergraphs to describe multiple targets. The characteristics of multiple correlations of multimodal data are comprehensively considered, and the similarity between multimodal data is comprehensively considered, and the semantic correlation between multimodal data is further mined. Combining hypergraph sorting and subspace learning to further mine semantic associations between cross-modal data can further improve cross-modal retrieval accuracy.