Joint Linear Discrimination and Graph Regularization for Task-Oriented Cross- Modal Retrieval
Jin Dai, Ying Chen · Journal of Computer-Aided Design & Computer Graphics · 2021
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.