A novel image classification algorithm using CNN on a small computation platform

Chaoyan Zhang; Baolong Guo; Yan Zheng; Cheng Li

doi:10.1117/12.2572981

12 June 2020 A novel image classification algorithm using CNN on a small computation platform

Chaoyan Zhang, Baolong Guo, Yan Zheng, Cheng Li

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 1151918 (2020) https://doi.org/10.1117/12.2572981
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

Image classification and recognition has always been a key issue in the field of computer vision. Not only has it achieved great development, but its research space has broad prospects. This paper first explains the advantages and disadvantages of the traditional image classification methods, and then introduces the development on the neural network model. The algorithm proposed in this paper is mainly based on the LeNet-5 network, and the experiment is performed on the CIFAR-10 dataset. The improved neural network is superior to original model in classification accuracy.

Citation Download Citation

Chaoyan Zhang, Baolong Guo, Yan Zheng, and Cheng Li "A novel image classification algorithm using CNN on a small computation platform", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 1151918 (12 June 2020); https://doi.org/10.1117/12.2572981

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available