ChemCLS: a framework for applying supervised and weakly supervised machine learning to better interpretable cultural relic classification

Ziyu Zhou; Sixiang Ye; Yiming Huang; Jiayue Zhang

doi:10.1117/12.2684738

21 July 2023 ChemCLS: a framework for applying supervised and weakly supervised machine learning to better interpretable cultural relic classification

Ziyu Zhou, Sixiang Ye, Yiming Huang, Jiayue Zhang

Author Affiliations +

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127171O (2023) https://doi.org/10.1117/12.2684738
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Machine learning algorithms advance the classification of cultural relics in recent studies. A part of the studies adopts image processing methods as a solution. We believe that the blurring of the image limits this approach. On the other hand, we notice the studies conducted from the perspective of chemical composition, which avoids the problem of blurring images and shows interpretability. In this paper, we follow the direction of classifying by chemical composition and aim to provide our method with better interpretability. Hence, we propose a strategy that combined both supervised and weakly supervised methods as the solution. It visualizes the decision tree of classifying the main classes of cultural relics at first and extracts the chemical composition of the subclasses with the pseudo-labeling according to limited subclass labels next. Besides interpretability, it also performs accurately in subclasses classification by using the radial basis function network and random forest. Our results indicate that our weakly supervised method achieves the closest reduction to a finer classification.

Citation Download Citation

Ziyu Zhou, Sixiang Ye, Yiming Huang, and Jiayue Zhang "ChemCLS: a framework for applying supervised and weakly supervised machine learning to better interpretable cultural relic classification", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127171O (21 July 2023); https://doi.org/10.1117/12.2684738

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