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Article

An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage

by
Antonio M. Rinaldi
*,†,
Cristiano Russo
and
Cristian Tommasino
Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Via Claudio, 21, 80125 Napoli, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Computers 2022, 11(12), 172; https://doi.org/10.3390/computers11120172
Submission received: 4 November 2022 / Revised: 23 November 2022 / Accepted: 27 November 2022 / Published: 30 November 2022
(This article belongs to the Special Issue Computational Science and Its Applications 2022)

Abstract

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.
Keywords: augmented reality; deep learning; linked open data; knowledge graph augmented reality; deep learning; linked open data; knowledge graph

Share and Cite

MDPI and ACS Style

Rinaldi, A.M.; Russo, C.; Tommasino, C. An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage. Computers 2022, 11, 172. https://doi.org/10.3390/computers11120172

AMA Style

Rinaldi AM, Russo C, Tommasino C. An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage. Computers. 2022; 11(12):172. https://doi.org/10.3390/computers11120172

Chicago/Turabian Style

Rinaldi, Antonio M., Cristiano Russo, and Cristian Tommasino. 2022. "An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage" Computers 11, no. 12: 172. https://doi.org/10.3390/computers11120172

APA Style

Rinaldi, A. M., Russo, C., & Tommasino, C. (2022). An Augmented Reality CBIR System Based on Multimedia Knowledge Graph and Deep Learning Techniques in Cultural Heritage. Computers, 11(12), 172. https://doi.org/10.3390/computers11120172

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