Evaluating the Visual Similarity of Southwest China's Ethnic Minority Brocade Based on Deep Learning
<|reference_start|>Evaluating the Visual Similarity of Southwest China's Ethnic Minority Brocade Based on Deep Learning: 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.<|reference_end|>
@article{liu2024evaluating,
    title={Evaluating the Visual Similarity of Southwest China's Ethnic Minority
  Brocade Based on Deep Learning},
    author={Shichen Liu, Huaxing Lu},
    journal={arXiv preprint arXiv:2408.14060},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14060},
    primaryClass={cs.CV}
}