Semi-supervised Coupled Dictionary Learning for Cross-modal Retrieval in Internet Images and Texts

Nowadays massive amount of images and texts has been emerging on the Internet, arousing the demand of effective cross-modal retrieval such as text-to-image search and image-to-text search. To eliminate the heterogeneity between the modalities of images and texts, the existing subspace learning methods try to learn a common latent subspace under which cross-modal matching can be performed. However, these methods usually require fully paired samples (images with corresponding texts) and also ignore the class label information along with the paired samples. This may inhibit these methods from learning an effective subspace since the correlations between two modalities are implicitly incorporated. Indeed, the class label information can reduce the semantic gap between different modalities and explicitly guide the subspace learning procedure. In addition, the large quantities of unpaired samples (images or texts) may provide useful side information to enrich the representations from learned subspace. Thus, in this paper we propose a novel model for cross-modal retrieval problem. It consists of 1) a semi-supervised coupled dictionary learning step to generate homogeneously sparse representations for different modalities based on both paired and unpaired samples; 2) a coupled feature mapping step to project the sparse representations of different modalities into a common subspace defined by class label information to perform cross-modal matching. Experiments on a large scale web image dataset MIRFlickr-1M with both fully paired and unpaired settings show the effectiveness of the proposed model on the cross-modal retrieval task.