Integrating distance metric learning into label propagation model for multi-label image annotation

Existing approaches for automatic image annotation usually suffer from two issues: (1) lacking a good quality distance metric for image semantic similarity measure; (2) rarely considering the correlation between labels assigned to each image. In this paper, we aim to resolve both of the problems simultaneously in a novel unified framework. Specifically, a proper distance metric is learned based on the structural SVM in a discriminative manner, which can optimize the ranking of the images induced by distances from a test image. Subsequently, a collaborative label propagation algorithm is leveraged to model the correlation between class labels in an explicit manner. Also, the learned metric is embedded in the propagation model. The integration of the two components leads to more accurate annotation results. The experiments conducted on the Corel dataset demonstrate the effectiveness of the proposed unified framework.