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

Shichen Liu, Huaxing Lu
8/26/2024
cs.CV

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Abstract

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.

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Cite this paper

@article{liu2024evaluating,
  title  = {Evaluating the Visual Similarity of Southwest China's Ethnic Minority Brocade Based on Deep Learning},
  author = {Shichen Liu and Huaxing Lu},
  year   = {2024},
  eprint = {2408.14060},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.14060v1}
}

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