Network Art Image Classification and Print Propagation Extraction Based on Depth Algorithm

Hao Zhang,Haimin Sun,Tianyang Yuan,Shalli Rani

doi:10.1155/2022/2546015

Abstract

In recent years, with the development of computer technology and the Internet, image databases have increased day by day, and the classification of image data has become one of the important research issues for obtaining image information. This article aims to study the role of depth algorithms in network art image classification and print propagation extraction. This article proposes a series of methods of image classification, print dissemination, and deep learning algorithms and also conducts corresponding experiments on the role of deep algorithms in image classification. The experimental results show that the neural network model based on the deep algorithm can effectively identify and classify network images, and its recognition accuracy is more than 80%. The image recognition method based on depth algorithm greatly improves the efficiency of image recognition.

Highlights

In order to efficiently manage and accurately classify image data, manual efficiency alone can no longer solve the problem
In Su et al.’s research, in order to optimize the classification of hyperspectral images and the selection and optimization of frequency bands, they proposed an extreme learning machine (ELM) method based on the firefly algorithm (FA) trigger [6]
The CNN image recognition model based on the depth algorithm is higher than other network models in image recognition rate, accuracy rate, and recognition speed

Summary

Introduction

In order to efficiently manage and accurately classify image data, manual efficiency alone can no longer solve the problem. In Su et al.’s research, in order to optimize the classification of hyperspectral images and the selection and optimization of frequency bands, they proposed an extreme learning machine (ELM) method based on the firefly algorithm (FA) trigger [6]. These researchers have done a lot of research on image classification and depth algorithms, they have neglected the related research on some problems existing in image classification and depth algorithms in their research. Corresponding research provides a certain theoretical basis for popularizing the application of depth algorithm in image classification

Image Classification

Image Recognition and Classification Experiment

Results of Image Recognition and Classification Experiment

Conclusions