Analysis of Image Evolution of Ancient Large Figurines Based on Deep Neural Network

Yingjian Zhao,Xin Ning

doi:10.1155/2022/7546562

Abstract

Unlike traditional image recognition technology, DL can automatically extract features and improve recognition accuracy by combining feature extraction and classification. The challenges and shortcomings of traditional image recognition methods are discussed in this article, as well as the development process and research status of DL. Related theories in image recognition based on deep learning (DL) are proposed, DL’s basic models and methods are analyzed, and related image data sets are demonstrated experimentally. Furthermore, because DL is typically used for large sample sets, this paper proposes an improved algorithm based on small samples, as well as a DNN-based analysis model for the evolution of ancient large figurine images. This model, when compared to the traditional neural network model, can speed up the network’s convergence speed and reduce training time to a certain extent. This model improves the rate of image recognition while lowering the error rate.

Highlights

The artistic genius was exerted on ritual and sacrificial vessels, and it formed a tradition with far-reaching influence [1]
convolutional neural network (CNN) is a deep learning (DL) method inspired by a biological visual cognitive mechanism, which has played an important role in the history of DL and is one of the first depth models with good performance
CNN is an improvement on the traditional neural network, which can be used to process data with similar grid structure

Summary

Introduction

The artistic genius was exerted on ritual and sacrificial vessels, and it formed a tradition with far-reaching influence [1]. Large-scale figurines in ancient China are very decorative. Since the Southern and Northern Dynasties, due to the development of Buddhist art, many large Buddhist figurines with decorative elements can be seen. They belong to religious themes, they are still created based on real life [3]. Researchers still do not know how DNN learns effective feature representation from large-scale data. For this reason, it is necessary to strengthen research on related content and further promote the development of related fields

Methods

Results

Conclusion