Evaluating the Visual Similarity of Southwest China's Ethnic Minority Brocade Based on Deep Learning
This work addresses the need for automated cultural heritage analysis for researchers and preservationists, but it is incremental as it applies an adapted deep learning method to a specific dataset.
This paper tackled the problem of evaluating visual similarity in ethnic minority brocade patterns from Southwest China using deep learning, achieving 98.7% accuracy with a customized SResNet-18 network that outperformed existing models like ResNet-18, VGGNet-16, and AlexNet.
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.