Learning Contextual Metrics for Automatic Image Annotation

The semantic contextual information is shown to be an important resource for improving the scene and image recognition, but is seldom explored in the literature of previous distance metric learning (DML) for images. In this work, we present a novel Contextual Metric Learning (CML) method for learning a set of contextual distance metrics for real world multi-label images. The relationships between classes are formulated as contextual constraints for the optimization framework to leverage the learning performance. In the experiment, we apply the proposed method for automatic image annotation task. The experimental results show that our approach outperforms the start-of-the-art DML algorithms.