Generalized Semi-supervised and Structured Subspace Learning for Cross-Modal Retrieval
Motivated by the fact that unlabeled data can be easily collected and help to exploit the correlations among different modalities, this paper proposes a novel method named generalized semi-supervised structured subspace learning (GSS-SL) for the task of cross-modal retrieval. First, to predict more relevant class labels for unlabeled data, we propose a label graph constraint that ensures the intrinsic geometric structures of different feature spaces consistent with that of label space. Second, considering that class labels directly reveal the semantic information of multimedia data, GSS-SL takes the label space as a linkage to model the correlations among different modalities. Concretely, the label graph constraint, label-linked loss function, and regularization are integrated into a joint minimization formulation to learn a discriminative common subspace. Finally, an efficient optimization algorithm is designed to alternately optimize multiple linear transformations for different modalities and update the class indicator matrices for unlabeled data. Furthermore, an arbitrary number of modalities can be solved in the proposed framework. Extensive experiments on three standard benchmark datasets demonstrate that GSS-SL outperforms previous methods on exploiting the correlations among different modalities.