Deep learning-based automatic annotation and online classification of remote multimedia images

In this paper, based on in-depth analysis of remote multimedia images, the automatic annotation and classification of graphics are tested and analyzed by algorithms of deep learning. To reduce the time of remote multimedia image labeling and online classification, and improve efficiency, we study the use of deep learning methods to automate annotation and online classification of remote multimedia images. An image is re-labeling algorithm based on modeling the correlation of hidden feature dimensions is proposed to improve the effect of hidden feature models by modeling the correlation between hid feature dimensions. The algorithm constructs the correlation between each pair of dimensions in the hidden features through the outer product operation to form a two-dimensional interactive graph. The information in the interaction graph is refined layer by layer by using the ability of the convolutional neural network to model local features, and finally, a representation of the correlation of all dimensions in the hidden features is formed to realize the re-labeling of social images. The experimental results show that this method can utilize the hidden feature information more effectively and improve the image re-labeling results. The light-weight feature extraction network significantly reduces the number of model parameters at the expense of a small amount of detection accuracy, and the FPN pyramid structure enhances the feature characterization ability of the feature extraction network. The performance is close to that of the Yolo algorithm.