A Systematic Approach for Developing a Robust Artwork Recognition Framework Using Smartphone Cameras

The provision of information encourages people to visit cultural sites more often. Exploiting the great potential of using smartphone cameras and egocentric vision, we describe the development of a robust artwork recognition algorithm to assist users when visiting an art space. The algorithm recogni...

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Bibliographic Details
Published in:Algorithms 2022-09, Vol.15 (9), p.305
Main Authors: Theodosiou, Zenonas, Thoma, Marios, Partaourides, Harris, Lanitis, Andreas
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Art galleries & museums
Artificial neural networks
artwork recognition
Cameras
Datasets
Deep learning
egocentric vision
Experimentation
Image retrieval
Literature reviews
Museum exhibits
Museums
Neural networks
Professionals
Radio frequency identification
Recognition
Robustness
Sculpture
Smart phones
smartphone app
Smartphones
User experience
User satisfaction
Virtual environments
visiting experience
Wearable computers
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Summary:The provision of information encourages people to visit cultural sites more often. Exploiting the great potential of using smartphone cameras and egocentric vision, we describe the development of a robust artwork recognition algorithm to assist users when visiting an art space. The algorithm recognizes artworks under any physical museum conditions, as well as camera point of views, making it suitable for different use scenarios towards an enhanced visiting experience. The algorithm was developed following a multiphase approach, including requirements gathering, experimentation in a virtual environment, development of the algorithm in real environment conditions, implementation of a demonstration smartphone app for artwork recognition and provision of assistive information, and its evaluation. During the algorithm development process, a convolutional neural network (CNN) model was trained for automatic artwork recognition using data collected in an art gallery, followed by extensive evaluations related to the parameters that may affect recognition accuracy, while the optimized algorithm was also evaluated through a dedicated app by a group of volunteers with promising results. The overall algorithm design and evaluation adopted for this work can also be applied in numerous applications, especially in cases where the algorithm performance under varying conditions and end-user satisfaction are critical factors.
ISSN:1999-4893
1999-4893
DOI:10.3390/a15090305