Classification and Model Method of Convolutional Features in Sketch Images Based on Deep Learning

摘要

Aiming at the poor convergence of the current sketch image classification and unable to meet the growing network needs, a classification and model method of convolution feature in sketch image based on deep learning is proposed. Based on the classification principle of deep learning, a classification experiment was carried out on the semantic features of sketch works through the analysis of convolution neural network, convolution feature model, convolution and sketch extraction boundary. The experimental results show that the proposed convolution classification and recognition method is better than the traditional classification method and has higher accuracy in dimensionality reduction and error rate detection than the traditional method. It can better meet the needs of network intelligent processing of sketch image feature classification.