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.

翻译：本文采用深度学习方法研究中国西南地区少数民族图案的视觉相似性。开发了一种定制的SResNet-18网络，在测试集上达到98.7%的准确率，其性能优于ResNet-18、VGGNet-16和AlexNet。利用余弦相似度、欧氏距离和曼哈顿距离三种度量方法对SResNet-18提取的特征向量进行评估。分析结果通过民族专题地图进行可视化呈现，揭示了民族图案与其地域分布之间的关联性。