Autonomous Classification of Digital Painting Images Based on Wireless Network

Abstract

With the advancement of digital technology, the precise classification and rapid search of digital painting is important to the creation of digital painting image digitization. In order to deeply study the construction of a self-service classification system based on wireless network, this paper uses the parameter sample insertion method, comparative analysis method, and deconstruction column method, analyzes the independent classification algorithm, and simplifies the algorithm. And finally, we realize and create an efficient system that can independently classify digital images. When studying the best points of the classification efficiency of classification systems, the number of training sets is 2000 and the number of test sets is 120. The input data is classified as 3 layers, and the learning rate of each layer is 1.5, 0.06, and the sparsity is 0.04. The results show that when the sparsity value of both layers of CRBM is taken as 0.02, the classification results reach the best, indicating that here is the best result of this experiment. Further studying the classification system stability, the image from the F1 training set is labeled as 0 and the network data is labeled as 1, thus providing a training two-way model, and the ratio between the training and test sets is 3 : 1. The classification accuracy is quite stable. With the addition of more training data, the classification accuracy remains at around 50%. This shows that the system stability is guaranteed. Based on wireless network technology, a complete system is designed to classify digital images.