Automatic image annotation via compact graph based semi-supervised learning

The insufficiency of labeled samples is major problem in automatic image annotation. However, unlabeled samples are readily available and abundant. Hence, semi-supervised learning methods, which utilize partly labeled samples and a large amount of unlabeled samples, have attracted increased attention in the field of image annotation. During the past decade, graph-based semi-supervised learning has been becoming one of the most important research areas in semi-supervised learning. In this paper, we propose a novel and effective graph based semi-supervised learning method for image annotation. The new method is derived by a compact graph that can well grasp the manifold structure. In addition, we theoretically prove that the proposed semi-supervised learning method can be analyzed under a regularized framework. It can also be easily extended to deal with out-of-sample data. Simulation results show that the proposed method can achieve better performance compared with other state-of-the-art graph based semi-supervised learning methods.