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.

Private data is easy to suffer from the attacks about data confidentiality, integrity and freshness. To resolve this problem, a secure data aggregation algorithm based on homomorphic Hash function was proposed, called HPDA (High-Efficiency Privacy Preserving Data Aggregation) algorithm. Firstly, it used homomorphic encryption scheme to provide data privacy-preserving. Secondly, it adopted homomorphic Hash function to verify the integrity and freshness of aggregated data. Finally, it reduced the communication overhead of the system by improved ID transmission mechanism. The theoretical analyses and experimental simulation results show that HPDA can effectively preserve data confidentiality, check data integrity, satisfy data freshness, and bring low communication overhead.