Semi-Supervised Cross-Modal Retrieval Based on Discriminative Comapping

Abstract

Most cross-modal retrieval methods based on subspace learning just focus on learning the projection matrices that map different modalities to a common subspace and pay less attention to the retrieval task specificity and class information. To address the two limitations and make full use of unlabelled data, we propose a novel semi-supervised method for cross-modal retrieval named modal-related retrieval based on discriminative comapping (MRRDC). The projection matrices are obtained to map multimodal data into a common subspace for different tasks. In the process of projection matrix learning, a linear discriminant constraint is introduced to preserve the original class information in different modal spaces. An iterative optimization algorithm based on label propagation is presented to solve the proposed joint learning formulations. The experimental results on several datasets demonstrate the superiority of our method compared with state-of-the-art subspace methods.