Existing image annotation algorithms can be roughly divided into four categories:the semantics based methods, the probability based methods, the matrix decomposition based methods and the graph learning based methods. Some representative algorithms for every category were introduced and the problem models and characteristics of these algorithms were analyzed. Then the main optimization methods of these algorithms were induced, and the common image datasets and the evaluation metrics of these algorithms were introduced. Finally, the main problems of automatic image annotation were pointed out, and the solutions to these problems were put forward. The analytical results show that the full use of complementary advantages of the current algorithms, or taking multi-disciplinary advantages may provide more efficient algorithm for automatic image annotation.