An Automatic Framework Recognizing the Relationships of Cultural Heritage

The field of cultural heritage has developed over a long period, accumulating a wealth of research findings. Researchers are now focusing on systematic relationships and taxonomic studies of heritage, exploring the underlying cultural information embedded within. Inspired by the fields of machine le...

Full description

Saved in:
Bibliographic Details
Main Authors: Zizhan Zhang, Zijun Zhou, Yingchun Cao
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10755094/
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1850064188589735936
author Zizhan Zhang
Zijun Zhou
Yingchun Cao
author_facet Zizhan Zhang
Zijun Zhou
Yingchun Cao
author_sort Zizhan Zhang
collection DOAJ
description The field of cultural heritage has developed over a long period, accumulating a wealth of research findings. Researchers are now focusing on systematic relationships and taxonomic studies of heritage, exploring the underlying cultural information embedded within. Inspired by the fields of machine learning and biology, we propose a research approach that combines data processing with unsupervised algorithms (“Feature Sparsity Module + N”), which can be utilized to unveil the systematic relationships of cultural heritage study subjects. We construct the Cultural Heritage Relationship Evaluation Framework (CHREF), framework using the structure of “FSM + PCA + HCA”, which offers a workflow characterized by interpretability, visualization capabilities, and automation. The framework utilizes the FSM module to transform the research subjects into matrices, employing PCA and HCA to obtain intuitive charts and reliable data results with minimal manual intervention. Additionally, we provide experiments and a user study on traditional Chinese brick kilns to validate the effectiveness and universality of the proposed framework. The “FSM + N” methodology and CHREF can provide tools for various stages of work in cultural heritage, making significant contributions to the digital development and database construction in the field of cultural heritage.
format Article
id doaj-art-8d03d0b2cffe4db6a91ec138bb5e0b28
institution DOAJ
issn 2169-3536
language English
publishDate 2024-01-01
publisher IEEE
record_format Article
series IEEE Access
spelling doaj-art-8d03d0b2cffe4db6a91ec138bb5e0b282025-08-20T02:49:22ZengIEEEIEEE Access2169-35362024-01-011217168917170510.1109/ACCESS.2024.350058410755094An Automatic Framework Recognizing the Relationships of Cultural HeritageZizhan Zhang0https://orcid.org/0009-0007-3637-939XZijun Zhou1https://orcid.org/0009-0004-2389-6409Yingchun Cao2https://orcid.org/0000-0003-0770-3342School of Architecture and Art, Hebei University of Architecture, Zhangjiakou, Hebei, ChinaSchool of Artificial Intelligence, Jilin University, Changchun, Jilin, ChinaSchool of Civil Engineering and Architecture, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, ChinaThe field of cultural heritage has developed over a long period, accumulating a wealth of research findings. Researchers are now focusing on systematic relationships and taxonomic studies of heritage, exploring the underlying cultural information embedded within. Inspired by the fields of machine learning and biology, we propose a research approach that combines data processing with unsupervised algorithms (“Feature Sparsity Module + N”), which can be utilized to unveil the systematic relationships of cultural heritage study subjects. We construct the Cultural Heritage Relationship Evaluation Framework (CHREF), framework using the structure of “FSM + PCA + HCA”, which offers a workflow characterized by interpretability, visualization capabilities, and automation. The framework utilizes the FSM module to transform the research subjects into matrices, employing PCA and HCA to obtain intuitive charts and reliable data results with minimal manual intervention. Additionally, we provide experiments and a user study on traditional Chinese brick kilns to validate the effectiveness and universality of the proposed framework. The “FSM + N” methodology and CHREF can provide tools for various stages of work in cultural heritage, making significant contributions to the digital development and database construction in the field of cultural heritage.https://ieeexplore.ieee.org/document/10755094/Cultural heritagemachine learninginterdisciplinary researchembeddingone-hot encodingprincipal component analysis
spellingShingle Zizhan Zhang
Zijun Zhou
Yingchun Cao
An Automatic Framework Recognizing the Relationships of Cultural Heritage
IEEE Access
Cultural heritage
machine learning
interdisciplinary research
embedding
one-hot encoding
principal component analysis
title An Automatic Framework Recognizing the Relationships of Cultural Heritage
title_full An Automatic Framework Recognizing the Relationships of Cultural Heritage
title_fullStr An Automatic Framework Recognizing the Relationships of Cultural Heritage
title_full_unstemmed An Automatic Framework Recognizing the Relationships of Cultural Heritage
title_short An Automatic Framework Recognizing the Relationships of Cultural Heritage
title_sort automatic framework recognizing the relationships of cultural heritage
topic Cultural heritage
machine learning
interdisciplinary research
embedding
one-hot encoding
principal component analysis
url https://ieeexplore.ieee.org/document/10755094/
work_keys_str_mv AT zizhanzhang anautomaticframeworkrecognizingtherelationshipsofculturalheritage
AT zijunzhou anautomaticframeworkrecognizingtherelationshipsofculturalheritage
AT yingchuncao anautomaticframeworkrecognizingtherelationshipsofculturalheritage
AT zizhanzhang automaticframeworkrecognizingtherelationshipsofculturalheritage
AT zijunzhou automaticframeworkrecognizingtherelationshipsofculturalheritage
AT yingchuncao automaticframeworkrecognizingtherelationshipsofculturalheritage