Image annotation via learning the image-label interrelations

The goal of image annotation is to automatically assign meaningful and content-related labels to the digital images by using machines. It is beneficial to image search and image sharing in social networks. Various methods for image annotation are proposed in last decade and they have gained much progress. However, most of them are not precise and fast enough for real-world applications. In this paper, we propose a novel and fast image annotation method via learning the image-label interrelation. The main idea of the proposed method is to predict labels by linearly propagating the label information through the image-label interrelation and the image similarities. Thus, we propose a model based on the regression between the label groundtruth and the propagated label information to learn the image-label interrelation. In addition, a label-biased regularization is integrated into our model to learn more effective and meaningful image-label interrelation. Finally, our model can be solved in closed form, therefore it achieves a fast learning process. Experimental results on three benchmark datasets demonstrate that our method shows the comparable performance with state-of-the-art methods and has faster learning time.