Aiming at the problem that the traditional image annotation model has long training time

sensitive to low-frequency words and other issues

this paper proposes a new automatic image annotation method based on Monte-Carlo dataset balance and robustness incremental extreme learning machine. First of all

training images of the public image library are segmented into different areas by this model and corresponding seed markup words are selected after segmentation

the areas are matched automatically based on comprehensive distance algorithm and the different keywords represent different areas. Then

for the huge difference of different annotated words' sizes in the public database

the Monte Carlo data set equalization algorithm is proposed to make the data size of each annotated word much the same. And a multi-scale feature fusion algorithm is proposed to extract effective features from different annotated images. Finally

the robustness incremental limit learning is proposed to improve the accuracy of the discriminant model for the problems of the consistency of the hidden layer nodes and the input vector weights existing in the traditional limit learning machine. The experimental results show that:compared with traditional algorithms of image automatic annotation

the methods proposed in this paper can implement the automatic image annotation quickly

and it is robust to low frequency words

and it is higher than most popular models of automatic image annotation in terms of average recall rate

average accuracy rate

comprehensive value and so on.