%0 Journal Article %A CAO Jianfang %A ZHANG Zibang %A ZHAO Aidi %T Application of convolutional neural network with threshold optimization in image annotation %D 2020 %R 10.11772/j.issn.1001-9081.2019111993 %J Journal of Computer Applications %P 1587-1592 %V %N %X

Ranking function based annotation may cause more or fewer labels according to the probability predicted by the model in multi-label image annotation. Therefore, a Convolutional Neural Network with THreshold OPtimization (CNN-THOP) model was proposed. The model consists of Convolutional Neural Network (CNN) and threshold optimization. Firstly, CNN was used to train a model, which was used to predict the image, so as to obtain the prediction probability, and Batch Normalization (BN) layer was added to the CNN to effectively accelerate the convergence. Secondly, threshold optimization was performed by the prediction probabilities of the test set images obtained by the proposed model. After the threshold optimization process, an optimal threshold was obtained for each kind of label, so as to obtain a set of optimal thresholds. Only when the prediction probability of this kind of label was greater than or equal to the best threshold of this kind of label, the image would be labeled with this label. In the labeling process, the CNN model and a set of optimal thresholds were added to achieve more flexible multi-label labeling of the image to be labeled. Through the verification on 8 000 images in the natural scene image dataset, experimental results show that CNN-THOP has about 20 percentage points improvement on average precision compared to Ranking Support Vector Machine (Rank-SVM), and is about 6 percentage points and 4 percentage points higher respectively than Convolutional Neural Network using Mean Square Error function (CNN-MSE) in average recall and F1 value respectively, and has the Complete Matching Degree (CMD) reached 64.75%, which proves that the proposed method is effective in automatic image annotation.

%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019111993