Study on Cross-media Information Retrieval Based on Common Subspace Classification Learning

The semantic similarity between two different media data can not be calculated directly because of the serious heterogeneous gap and semantic gap between them, which affects the implementation and effect of cross media retrieval.Although the common space learning can achieve cross media semantic association and retrieval, the retrieval performance is not satisfied.The main reason is that it uses common feature extraction technology and general classification algorithm to implement semantic correlation and match.Aiming at this problem, the study proposes a novel cross media correlation method called Stacking-DSCM-WR for cross media retrieval between documents and images.WR means that text feature extraction is based on word-embedding technique and the image feature extraction is based on ResNet technique.DSCM means that the deep semantic correlation and match technology is exploited to project data of different modalities into a common subspace.Stacking is a kind of ensemble lear-ning algorithm.It is employed