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Compare the performance of the models in art classification
Because large numbers of artworks are preserved in museums and galleries, much work must be done to classify these works into genres, styles and artists. Recent technological advancements have enabled an increasing number of artworks to be digitized. Thus, it is necessary to teach computers to analy...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954342/ https://www.ncbi.nlm.nih.gov/pubmed/33711046 http://dx.doi.org/10.1371/journal.pone.0248414 |
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author | Zhao, Wentao Zhou, Dalin Qiu, Xinguo Jiang, Wei |
author_facet | Zhao, Wentao Zhou, Dalin Qiu, Xinguo Jiang, Wei |
author_sort | Zhao, Wentao |
collection | PubMed |
description | Because large numbers of artworks are preserved in museums and galleries, much work must be done to classify these works into genres, styles and artists. Recent technological advancements have enabled an increasing number of artworks to be digitized. Thus, it is necessary to teach computers to analyze (e.g., classify and annotate) art to assist people in performing such tasks. In this study, we tested 7 different models on 3 different datasets under the same experimental setup to compare their art classification performances when either using or not using transfer learning. The models were compared based on their abilities for classifying genres, styles and artists. Comparing the result with previous work shows that the model performance can be effectively improved by optimizing the model structure, and our results achieve state-of-the-art performance in all classification tasks with three datasets. In addition, we visualized the process of style and genre classification to help us understand the difficulties that computers have when tasked with classifying art. Finally, we used the trained models described above to perform similarity searches and obtained performance improvements. |
format | Online Article Text |
id | pubmed-7954342 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-79543422021-03-22 Compare the performance of the models in art classification Zhao, Wentao Zhou, Dalin Qiu, Xinguo Jiang, Wei PLoS One Research Article Because large numbers of artworks are preserved in museums and galleries, much work must be done to classify these works into genres, styles and artists. Recent technological advancements have enabled an increasing number of artworks to be digitized. Thus, it is necessary to teach computers to analyze (e.g., classify and annotate) art to assist people in performing such tasks. In this study, we tested 7 different models on 3 different datasets under the same experimental setup to compare their art classification performances when either using or not using transfer learning. The models were compared based on their abilities for classifying genres, styles and artists. Comparing the result with previous work shows that the model performance can be effectively improved by optimizing the model structure, and our results achieve state-of-the-art performance in all classification tasks with three datasets. In addition, we visualized the process of style and genre classification to help us understand the difficulties that computers have when tasked with classifying art. Finally, we used the trained models described above to perform similarity searches and obtained performance improvements. Public Library of Science 2021-03-12 /pmc/articles/PMC7954342/ /pubmed/33711046 http://dx.doi.org/10.1371/journal.pone.0248414 Text en © 2021 Zhao et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhao, Wentao Zhou, Dalin Qiu, Xinguo Jiang, Wei Compare the performance of the models in art classification |
title | Compare the performance of the models in art classification |
title_full | Compare the performance of the models in art classification |
title_fullStr | Compare the performance of the models in art classification |
title_full_unstemmed | Compare the performance of the models in art classification |
title_short | Compare the performance of the models in art classification |
title_sort | compare the performance of the models in art classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954342/ https://www.ncbi.nlm.nih.gov/pubmed/33711046 http://dx.doi.org/10.1371/journal.pone.0248414 |
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