Abstract: Aiming at the problem of insufficient consideration of the differences between different retrieval tasks and semantic consistency of retrieval-modal data in the current common subspace based cross-modal retrieval algorithms,a task-oriented cross-modal retrieval based on jointing linear discrimination and graph regularization is proposed.The approach constructed different mapping mechanisms for retrieval tasks in a joint learning framework,and mapped multi-modal data into common subspaces for similarity measuring.During the learning process,correlation analysis and single-modal semantic regression were combined to preserve the correlation between paired data and enhance the semantic accuracy of query-modal data.Simultaneously,linear discrimination analysis was utilized to ensure semantic consistency of retrieval-modal samples.The approach also constructed local neighbor graphs for multi-modal data to preserve structural information,which can improve the retrieval performance.Experiments results on two cross-modal datasets,namely Wikipedia and Pascal Sentence showed that the average mAP value on different retrieval tasks of the proposed method had respectively increased by 1.0%‒16.0%and 1.2%‒14.0%compared with the twelve existing methods.