Multi-View Metric Learning for Multi-Label Image Classification

Multi-label image classification is a very challenging task, where data are often associated with multiple labels and represented with multiple views. In this paper, we propose a novel multi-view distance metric learning approach to dealing with the multi-label image classification problem. In particular, we attempt to concatenate multiple types of features after learning one optimal distance metric for each view, so as to obtain a better joint representation across multi-view spaces. To preserve the intrinsic geometric structure of the data in the low-dimensional feature space, we introduce a manifold regularization with the adjacency graph being constructed based on all labels. Moreover, we learn another distance metric to capture the dependency of labels, which can further improve the classification performance. Experimental results on publicly available image datasets demonstrate that our method achieves superior performance, compared with the state-of-the-arts.