Title: Research on spiking neural network in art visual image classification

Authors: Yiping Zhang

Addresses: Sino-German Institute of Design and Communication, Zhejiang Wanli University, Ningbo, 315100, China

Abstract: In the visual processing of artistic images, traditional CNN has a high resource demand, and SNN can solve this problem. The article selects SNN as the method for artistic visual image processing and combines it with CNN to simplify model training. After CNN adjustment and feedback adjustment algorithm processing, the classification accuracy of SNN can be improved. The results show that the accuracy of the adjusted CNN model is 80.25% and 79.60%, respectively, with an average training accuracy difference of 3.32%. Under the same pooling combination, the accuracy of the model with 11 and 12 iterations is 68.00% and 66.02%, respectively. The average classification accuracy of SNN is 78.80%, slightly lower than the adjusted CNN. SNN has a power consumption of approximately 0.0039 W per second in processing 742 images. The correlation classification method used in the article can reduce power consumption and has a high classification accuracy.

Keywords: spiking neural network; SNN; convolutional neural network; CNN; artistic visual image; accuracy; classification.

DOI: 10.1504/IJCISTUDIES.2023.137809

International Journal of Computational Intelligence Studies, 2023 Vol.12 No.3/4, pp.206 - 222

Received: 16 Sep 2022
Accepted: 11 May 2023
Published online: 05 Apr 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article