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Aim & Scope

Cultural heritage, in particular fine art, has invaluable importance for the cultural, historic, and economic growth of our societies. Fine art is developed primarily for aesthetic purposes, and it is mainly concerned with paintings, sculptures, and architectures. In the last few years, due to technology improvements and drastically declining costs, a large-scale digitization effort has been made, leading to a growing availability of large digitized fine art collections. This availability, along with the recent advancements in pattern recognition and computer vision, has opened new opportunities for computer science researchers to assist the art community with automatic tools to analyse and further understand fine arts. Among the other benefits, a deeper understanding of fine arts has the potential to make them more accessible to a wider population, both in terms of fruition and creation, thus supporting the spread of culture.

The ability to recognize meaningful patterns in fine art inherently falls within the domain of human perception, and this perception can be extremely hard to conceptualize. Thus, visual-related features, such as those automatically learned by deep learning models, can be the key to tackling problems of extracting useful representations from low-level colour and texture features. These representations can assist in various art-related tasks, ranging from object detection in paintings to artistic style categorization, useful for examples in museum and art gallery websites.

This special issue will provide the researchers from diverse areas such as pattern recognition, computer vision, artificial intelligence, and image processing, with the opportunity to present advancements in the state of the art, innovative research, ongoing projects, and academic and industrial reports on the application of visual pattern extraction and recognition for the better understanding and fruition of fine arts.

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Topics include, but are not limited to:
- Application of machine learning and deep learning to cultural heritage
- Computer vision and multimedia data
- Generative adversarial networks for artistic data
- Augmented and virtual reality for cultural heritage
- 3D reconstruction of historical artifacts
- Historical document analysis
- Content-based retrieval in the art domain
- Speech, audio, and music analysis from historical archives
- Digitally enriched museum visits
- Smart interactive experiences in cultural sites
- Projects, products, or prototypes for cultural heritage restoration, preservation, and fruition

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Submission Deadline (*EXTENDED*): 31 May 2021
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Submit your paper to manuscript submission and peer review site via the following link:
-- https://www.mdpi.com/journal/jimaging/special_issues/faper2020 --

The Article Processing Charge (APC) for publication in the MDPI Journal of Imaging is 1000 CHF (Swiss Francs).

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Editor-in-Chief
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Prof. Gonzalo Pajares Martinsanz, Universitad Complutense de Madrid, Spain

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Guest Editors
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Giovanna Castellano, Universita' di Bari, Italy
Gennaro Vessio, Universita' di Bari, Italy
Fabio Bellavia, Universita' di Palermo, Italy