基于LDA和卷积神经网络的半监督图像标注方法

随着智能设备的不断出现,图像数量急速增加,但是很多图像因为没有被标注所以未被充分利用.为了能够使该问题得到较好解决,提出了基于LDA和卷积神经网络的半监督图像标注方法.首先把图像训练集中的所有文字信息放入LDA中,生成图像的文字标注词;然后使用卷积神经网络获得图像的高层视觉特征,同时用加入注意力机制和修改损失函数的方法...

A semi supervised image annotation method based on LDA and convolutional neural network

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.