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Evaluating the Visual Similarity of Southwest China's Ethnic Minority Brocade Based on Deep Learning

2024-08-26 · Shichen Liu, Huaxing Lu

This paper employs deep learning methods to investigate the visual similarity of ethnic minority patterns in Southwest China. A customized SResNet-18 network was developed, achieving an accuracy of 98.7% on the test set, outperforming ResNet-18, VGGNet-16, and AlexNet. The extracted feature vectors from SResNet-18 were evaluated using three metrics: cosine similarity, Euclidean distance, and Manhattan distance. The analysis results were visually represented on an ethnic thematic map, highlighting the connections between ethnic patterns and their regional distributions.

📄 PDF Abstract BibTeX arXiv:2408.14060

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