Similarity Analysis Model with 6CH ResNet Structure


JunHyeok Go, Nammee Moon, Journal of Information Processing Systems Vol. 20, No. 5, pp. 675-683, Oct. 2024  

https://doi.org/10.3745/JIPS.01.0109
Keywords: Convolutional Neural Network (CNN), Image Similarity, Large Waste
Fulltext:

Abstract

Large-scale waste similarity analysis is crucial for automating waste management on a large scale. It involves confirming the match between waste discharged from homes and that collected by agencies, which is essential for a stable automated system. This paper compares feature extraction methods for similarity measurement, including the scale-invariant feature transform (SIFT) algorithm with added HSV color features, convolutional neural network-based encoders, and a modified 6-channel (6CH) ResNet for end-to-end learning. The results demonstrate that the 6CH ResNet achieves up to 4.9% higher accuracy than both the basic SIFT method and encoders, as well as the SIFT algorithm with HSV color features. Implementing the 6CH ResNet in automated waste management systems can enhance object similarity measurement while using fewer computing resources.


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Cite this article
[APA Style]
Go, J. & Moon, N. (2024). Similarity Analysis Model with 6CH ResNet Structure. Journal of Information Processing Systems, 20(5), 675-683. DOI: 10.3745/JIPS.01.0109.

[IEEE Style]
J. Go and N. Moon, "Similarity Analysis Model with 6CH ResNet Structure," Journal of Information Processing Systems, vol. 20, no. 5, pp. 675-683, 2024. DOI: 10.3745/JIPS.01.0109.

[ACM Style]
JunHyeok Go and Nammee Moon. 2024. Similarity Analysis Model with 6CH ResNet Structure. Journal of Information Processing Systems, 20, 5, (2024), 675-683. DOI: 10.3745/JIPS.01.0109.