Image annotation using multi-label correlated Green's function

Image annotation has been an active research topic in the recent years due to its potentially large impact on both image understanding and web/database image search. In this paper, we target at solving the automatic image annotation problem in a novel semi-supervised learning framework. A novel multi-label correlated Green's function approach is proposed to annotate images over a graph. The correlations among labels are integrated into the objective function which improves the performance significantly. We also propose a new adaptive decision boundary method for multi-label assignment to deal with the difficulty of label assignment in most of the existing rank-based multi-label classification algorithms. Instead of setting the threshold heuristically or by experience, our method principally compute it upon the prior knowledge in the training data. We perform our methods on three commonly used image annotation testing data sets. Experimental results show significant improvements on classification performance over four other state-of-the-art methods. As a general semi-supervised learning framework, other local feature based image annotation methods could be easily incorporated into our framework to improve the performance.