Semi-supervised dual low-rank feature mapping for multi-label image annotation

Automatic image annotation as a typical multi-label learning problem, has gained extensive attention in recent years owing to its application in image semantic understanding and relevant disciplines. Nevertheless, existing annotation methods share the same challenge that labels annotated on the training images are usually incomplete and unclean, while the need for adequate training data is costly and unrealistic. Being aware of this, we propose a dual low-rank regularized multi-label learning model under a graph regularized semi-supervised learning framework, which can effectively capture the label correlations in the learned feature space, and enforce the label matrix be self-recovered in label space as well. To be specific, the proposed approach firstly puts forward a label matrix refinement approach, by introducing a label coefficient matrix to build a linear self-recovery model. Then, graph Laplacian regularization is introduced to make use of a large number of unlabeled images by enforcing the local geometric structure on both labeled and unlabeled images. Lastly, we exploit dual trace norm regularization on both feature mapping matrix and self-recovery coefficient matrix to capture the correlations among different labels in both feature space and label space, and control the model complexity as well. Empirical studies on four real-world image datasets demonstrate the effectiveness and efficiency of the proposed framework.