关键词: LDA, 卷积神经网络, 注意力机制, 半监督学习

Abstract: With the continuous emergence of intelligent devices, the number of pictures increases rapidly. However, many images are not fully utilized because they are not labeled. In order to solve this problem, a semi supervised image annotation method based on LDA and convolutional neural network is proposed. Firstly, all text information in the image training set is put into LDA to generate text tagging words. Secondly, the convolutional neural network is used to obtain the high-level visual features of the image, and the convolutional neural network is optimized by adding attention mechanism and modifying loss function. Thirdly, the label words generated by LDA are combined with the high-level visual features of the obtained image, and the semi supervised learning is used to complete the model training. Finally, the correlation between the tagging words and the prediction results using the final model are combined to complete the final tagging of the image. Comparative experiments on the IAPR TC-12 image data set show that the proposed labeling method is more accurate.

Key words: LDA, convolutional neural network, attention mechanism, semi supervised learning