An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage
In the last few years, the spreading of new technologies, such as augmented reality (AR), has been changing our way of life. Notably, AR technologies have different applications in the cultural heritage realm, improving available information for a user while visiting museums, art exhibits, or genera...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/11/12/172 |
| _version_ | 1797460791824220160 |
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| author | Antonio M. Rinaldi Cristiano Russo Cristian Tommasino |
| author_facet | Antonio M. Rinaldi Cristiano Russo Cristian Tommasino |
| author_sort | Antonio M. Rinaldi |
| collection | DOAJ |
| description | In the last few years, the spreading of new technologies, such as augmented reality (AR), has been changing our way of life. Notably, AR technologies have different applications in the cultural heritage realm, improving available information for a user while visiting museums, art exhibits, or generally a city. Moreover, the spread of new and more powerful mobile devices jointly with virtual reality (VR) visors contributes to the spread of AR in cultural heritage. This work presents an augmented reality mobile system based on content-based image analysis techniques and linked open data to improve user knowledge about cultural heritage. In particular, we explore the uses of traditional feature extraction methods and a new way to extract them employing deep learning techniques. Furthermore, we conduct a rigorous experimental analysis to recognize the best method to extract accurate multimedia features for cultural heritage analysis. Eventually, experiments show that our approach achieves good results with respect to different standard measures. |
| first_indexed | 2024-03-09T17:10:07Z |
| format | Article |
| id | doaj.art-3419ed6a98e64912b8161f8719bec1db |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T17:10:07Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-3419ed6a98e64912b8161f8719bec1db2023-11-24T14:07:23ZengMDPI AGComputers2073-431X2022-11-01111217210.3390/computers11120172An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural HeritageAntonio M. Rinaldi0Cristiano Russo1Cristian Tommasino2Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Via Claudio, 21, 80125 Napoli, ItalyDepartment of Electrical Engineering and Information Technology, University of Napoli Federico II, Via Claudio, 21, 80125 Napoli, ItalyDepartment of Electrical Engineering and Information Technology, University of Napoli Federico II, Via Claudio, 21, 80125 Napoli, ItalyIn the last few years, the spreading of new technologies, such as augmented reality (AR), has been changing our way of life. Notably, AR technologies have different applications in the cultural heritage realm, improving available information for a user while visiting museums, art exhibits, or generally a city. Moreover, the spread of new and more powerful mobile devices jointly with virtual reality (VR) visors contributes to the spread of AR in cultural heritage. This work presents an augmented reality mobile system based on content-based image analysis techniques and linked open data to improve user knowledge about cultural heritage. In particular, we explore the uses of traditional feature extraction methods and a new way to extract them employing deep learning techniques. Furthermore, we conduct a rigorous experimental analysis to recognize the best method to extract accurate multimedia features for cultural heritage analysis. Eventually, experiments show that our approach achieves good results with respect to different standard measures.https://www.mdpi.com/2073-431X/11/12/172augmented realitydeep learninglinked open dataknowledge graph |
| spellingShingle | Antonio M. Rinaldi Cristiano Russo Cristian Tommasino An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage Computers augmented reality deep learning linked open data knowledge graph |
| title | An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage |
| title_full | An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage |
| title_fullStr | An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage |
| title_full_unstemmed | An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage |
| title_short | An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage |
| title_sort | augmented reality cbir system based on multimedia knowledge graph and deep learning techniques in cultural heritage |
| topic | augmented reality deep learning linked open data knowledge graph |
| url | https://www.mdpi.com/2073-431X/11/12/172 |
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