Image Classification Based on Deep Learning

Hanyang Tan

2024

Abstract

Image classification technology, as a core research direction in the field of computer vision, has become the focus of widespread attention among researchers with the development of deep learning technology. Although convolutional neural networks (CNN) have made revolutionary progress in image processing, there are still problems such as overfitting and the complexity of handling diverse data sets. This paper presents a hybrid model composed of a Convolutional Neural Network (CNN) module and a time-frequency composite weighting module. The CNN module effectively performs deep feature extraction, while the time-frequency composite weighting module is capable of achieving better performance. Through experimental verification on CIFAR 10, this paper demonstrates the excellent performance of the hybrid model on image classification tasks, with an accuracy of 90%. The results of this paper not only prove the effectiveness of combining different deep learning architectures to improve image classification accuracy, but also provide new ideas and methods for the development of future image processing technology.

Paper Citation

in Harvard Style

Tan H. (2024). Image Classification Based on Deep Learning. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 313-317. DOI: 10.5220/0012835500004547

in Bibtex Style

@conference{icdse24,
author={Hanyang Tan},
title={Image Classification Based on Deep Learning},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={313-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012835500004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Image Classification Based on Deep Learning
SN - 978-989-758-690-3
AU - Tan H.
PY - 2024
SP - 313
EP - 317
DO - 10.5220/0012835500004547
PB - SciTePress