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Hauptverfasser: Liu, Shichen, Lu, Huaxing
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2408.14060
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author Liu, Shichen
Lu, Huaxing
author_facet Liu, Shichen
Lu, Huaxing
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2408_14060
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Evaluating the Visual Similarity of Southwest China's Ethnic Minority Brocade Based on Deep Learning
Liu, Shichen
Lu, Huaxing
Computer Vision and Pattern Recognition
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.
title Evaluating the Visual Similarity of Southwest China's Ethnic Minority Brocade Based on Deep Learning
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2408.14060