Cross-media semantic retrieval with deep canonical correlation analysis

The cross-media retrieval with canonical correlation analysis (CCA) is a method to map different media features to the largest correlation isomorphism subspace through the canonical correlation analysis, and compare the similarity between cross-media data in the subspace. However CCA is a linear model and can not adequately exploit the complex correlation between cross-media data. The structure of the traditional deep canonical correlation analysis (DCCA) is improved, and the latent dirichlet allocation (LDA) is used to discover the semantic information in the text data and learns the semantic mapping. The cross-media correlation learning with deep canonical correlation analysis (CMC-DCCA) and the cross-media semantic correlation retrieval (CMSCR) are proposed. Experiments on the Wikipedia text image dataset shows that the CMC-DCCA model can mine the complex correlation between cross-media data better, and that CMSCR has better performance in cross-media retrieval.