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Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning
Computer-aided classification serves as the basis of virtual cultural relic management and display. The majority of the existing cultural relic classification methods require labelling of the samples of the dataset; however, in practical applications, there is often a lack of category labels of samp...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712925/ https://www.ncbi.nlm.nih.gov/pubmed/33287058 http://dx.doi.org/10.3390/e22111290 |
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author | Gao, Hongjuan Geng, Guohua Zeng, Sheng |
author_facet | Gao, Hongjuan Geng, Guohua Zeng, Sheng |
author_sort | Gao, Hongjuan |
collection | PubMed |
description | Computer-aided classification serves as the basis of virtual cultural relic management and display. The majority of the existing cultural relic classification methods require labelling of the samples of the dataset; however, in practical applications, there is often a lack of category labels of samples or an uneven distribution of samples of different categories. To solve this problem, we propose a 3D cultural relic classification method based on a low dimensional descriptor and unsupervised learning. First, the scale-invariant heat kernel signature (Si-HKS) was computed. The heat kernel signature denotes the heat flow of any two vertices across a 3D shape and the heat diffusion propagation is governed by the heat equation. Secondly, the Bag-of-Words (BoW) mechanism was utilized to transform the Si-HKS descriptor into a low-dimensional feature tensor, named a SiHKS-BoW descriptor that is related to entropy. Finally, we applied an unsupervised learning algorithm, called MKDSIF-FCM, to conduct the classification task. A dataset consisting of 3D models from 41 Tang tri-color Hu terracotta Eures was utilized to validate the effectiveness of the proposed method. A series of experiments demonstrated that the SiHKS-BoW descriptor along with the MKDSIF-FCM algorithm showed the best classification accuracy, up to 99.41%, which is a solution for an actual case with the absence of category labels and an uneven distribution of different categories of data. The present work promotes the application of virtual reality in digital projects and enriches the content of digital archaeology. |
format | Online Article Text |
id | pubmed-7712925 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77129252021-02-24 Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning Gao, Hongjuan Geng, Guohua Zeng, Sheng Entropy (Basel) Article Computer-aided classification serves as the basis of virtual cultural relic management and display. The majority of the existing cultural relic classification methods require labelling of the samples of the dataset; however, in practical applications, there is often a lack of category labels of samples or an uneven distribution of samples of different categories. To solve this problem, we propose a 3D cultural relic classification method based on a low dimensional descriptor and unsupervised learning. First, the scale-invariant heat kernel signature (Si-HKS) was computed. The heat kernel signature denotes the heat flow of any two vertices across a 3D shape and the heat diffusion propagation is governed by the heat equation. Secondly, the Bag-of-Words (BoW) mechanism was utilized to transform the Si-HKS descriptor into a low-dimensional feature tensor, named a SiHKS-BoW descriptor that is related to entropy. Finally, we applied an unsupervised learning algorithm, called MKDSIF-FCM, to conduct the classification task. A dataset consisting of 3D models from 41 Tang tri-color Hu terracotta Eures was utilized to validate the effectiveness of the proposed method. A series of experiments demonstrated that the SiHKS-BoW descriptor along with the MKDSIF-FCM algorithm showed the best classification accuracy, up to 99.41%, which is a solution for an actual case with the absence of category labels and an uneven distribution of different categories of data. The present work promotes the application of virtual reality in digital projects and enriches the content of digital archaeology. MDPI 2020-11-13 /pmc/articles/PMC7712925/ /pubmed/33287058 http://dx.doi.org/10.3390/e22111290 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gao, Hongjuan Geng, Guohua Zeng, Sheng Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning |
title | Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning |
title_full | Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning |
title_fullStr | Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning |
title_full_unstemmed | Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning |
title_short | Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning |
title_sort | approach for 3d cultural relic classification based on a low-dimensional descriptor and unsupervised learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712925/ https://www.ncbi.nlm.nih.gov/pubmed/33287058 http://dx.doi.org/10.3390/e22111290 |
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