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J. Imaging
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Methodologies and Applications of Image Understanding in Cultural and Artistic...
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Methodologies and Applications of Image Understanding in Cultural and Artistic Heritage

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Computer Vision and Pattern Recognition".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 6924

Special Issue Editors

Dr. Eva Cetinic

E-Mail Website
Guest Editor
Digital Society Initiative, University of Zurich, Rämistrasse 69, 8001 Zürich, Switzerland
Interests: digital arts and humanities; computer vision; multimodal deep learning; explainable and human-centered AI
Dr. Sinem Aslan

E-Mail Website
Guest Editor
Department of Historical Studies, University of Milan, Via Festa del Perdono 7, 20122 Milano, Italy
Interests: computer vision; deep learning; image analysis of visual art and cultural heritage

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to the various challenges and possibilities, both in terms of methodology and application, that emerge when computational methods are used to study, analyze, and interpret images in the context of art and culture. 

What does it mean to understand an image? The application of computational methods, such as object detection, segmentation, or classification, to images is usually motivated by the need to solve a particular problem. In the context of computer vision and deep learning, these methods are usually applied to general-purpose image datasets (ImageNet, MS Coco, etc.) or domain-specific image datasets (medical, satellite, traffic, etc.). Images related to art or cultural heritage may also represent another type of domain-specific image dataset. However, understanding an image becomes a much more complex task if we aim to integrate computational approaches of image understanding with the methodological and theoretical frameworks of traditional disciplines dedicated to the study of images in art and culture (e.g., art history and cultural and visual studies).

This Special Issue invites contributions that address different aspects of image understanding with the common goal of bridging cross-disciplinary gaps.

The topics of interest include, but are not limited to, the following:

  • Computational analysis of images as visual cultural artifacts;
  • Contextually meaningful image searches and similarity retrieval in artwork collections;
  • Deep learning-based approaches for studying artwork images;
  • Computer vision applications in cultural heritage;
  • Multimodal deep learning for image understanding;
  • Understanding AI-generated images in the context of art and culture.

Dr. Eva Cetinic
Dr. Sinem Aslan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer vision
  • deep learning
  • artwork analysis
  • cultural heritage
  • generative AI
  • multimodality
  • cultural AI
  • art datasets
  • digital art history
  • digital visual studies

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

20 pages, 49386 KiB  
Article
On the Dynamism of Paintings Through the Distribution of Edge Directions
by Adrien Deliege, Maria Giulia Dondero and Enzo D’Armenio
J. Imaging 2024, 10(11), 276; https://doi.org/10.3390/jimaging10110276 - 1 Nov 2024
Viewed by 831
Abstract
The digitization of artworks has recently offered new computational perspectives on the study of art history. While much of the focus has been on classifying styles or identifying objects, the analysis of more abstract concepts, such as the perception of motion or dynamism [...] Read more.
The digitization of artworks has recently offered new computational perspectives on the study of art history. While much of the focus has been on classifying styles or identifying objects, the analysis of more abstract concepts, such as the perception of motion or dynamism in still images, remains largely unexplored. Semioticians and artists have long explored the representation of dynamism in still images, but they often did so through theoretical frameworks or visual techniques, without a quantitative approach to measuring it. This paper proposes a method for computing and comparing the dynamism of paintings through edge detection. Our approach is based on the idea that the dynamism of a painting can be quantified by analyzing the edges in the image, whose distribution can be used to identify patterns and trends across artists and movements. We demonstrate the applicability of our method in three key areas: studying the temporal evolution of dynamism across different artistic styles, as well as within the works of a single artist (Wassily Kandinsky), visualizing and clustering a large database of abstract paintings through PixPlot, and retrieving similarly dynamic images. We show that the dynamism of a painting can be effectively quantified and visualized using edge detection techniques, providing new insights into the study of visual culture. Full article
(This article belongs to the Special Issue Methodologies and Applications of Image Understanding in Cultural and Artistic Heritage)
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21 pages, 11701 KiB  
Article
GOYA: Leveraging Generative Art for Content-Style Disentanglement
by Yankun Wu, Yuta Nakashima and Noa Garcia
J. Imaging 2024, 10(7), 156; https://doi.org/10.3390/jimaging10070156 - 26 Jun 2024
Viewed by 1528
Abstract
The content-style duality is a fundamental element in art. These two dimensions can be easily differentiated by humans: content refers to the objects and concepts in an artwork, and style to the way it looks. Yet, we have not found a way to [...] Read more.
The content-style duality is a fundamental element in art. These two dimensions can be easily differentiated by humans: content refers to the objects and concepts in an artwork, and style to the way it looks. Yet, we have not found a way to fully capture this duality with visual representations. While style transfer captures the visual appearance of a single artwork, it fails to generalize to larger sets. Similarly, supervised classification-based methods are impractical since the perception of style lies on a spectrum and not on categorical labels. We thus present GOYA, which captures the artistic knowledge of a cutting-edge generative model for disentangling content and style in art. Experiments show that GOYA explicitly learns to represent the two artistic dimensions (content and style) of the original artistic image, paving the way for leveraging generative models in art analysis. Full article
(This article belongs to the Special Issue Methodologies and Applications of Image Understanding in Cultural and Artistic Heritage)
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12 pages, 7865 KiB  
Article
Weakly Supervised SVM-Enhanced SAM Pipeline for Stone-by-Stone Segmentation of the Masonry of the Loire Valley Castles
by Stuardo Lucho, Sylvie Treuillet, Xavier Desquesnes, Remy Leconge and Xavier Brunetaud
J. Imaging 2024, 10(6), 148; https://doi.org/10.3390/jimaging10060148 - 19 Jun 2024
Viewed by 1011
Abstract
The preservation of historical monuments presents a formidable challenge, particularly in monitoring the deterioration of building materials over time. Chateau de Chambord’s facade suffers from common issues such as flaking and spalling, which require meticulous stone and joint mapping from experts manually for [...] Read more.
The preservation of historical monuments presents a formidable challenge, particularly in monitoring the deterioration of building materials over time. Chateau de Chambord’s facade suffers from common issues such as flaking and spalling, which require meticulous stone and joint mapping from experts manually for restoration efforts. Advancements in computer vision have allowed machine-learning models to help in the automatic segmentation process. In this research, a custom architecture defined as SAM-SVM is proposed, to perform stone segmentation, based on the Segment Anything Model (SAM) and Support Vector Machines (SVM). By exploiting the zero-shot learning capabilities of SAM and its customizable input parameters, we obtain segmentation mask for stones and joints, which are then classified using SVM. Two more SAMs (three in total) are used, depending on how many stones are left to segment. Through extensive experimentation and evaluation, supported by computer vision methods, the proposed architecture achieves a Dice coefficient of 85%. Our results highlight the potential of SAM in cultural heritage conservation, providing a scalable and efficient solution for stone segmentation in historic monuments. This research contributes valuable insights and methodologies to the ongoing conservation efforts of Château de Chambord and could be extrapolated to other monuments. Full article
(This article belongs to the Special Issue Methodologies and Applications of Image Understanding in Cultural and Artistic Heritage)
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20 pages, 25770 KiB  
Article
Exploring Emotional Stimuli Detection in Artworks: A Benchmark Dataset and Baselines Evaluation
by Tianwei Chen, Noa Garcia, Liangzhi Li and Yuta Nakashima
J. Imaging 2024, 10(6), 136; https://doi.org/10.3390/jimaging10060136 - 4 Jun 2024
Viewed by 1410
Abstract
We introduce an emotional stimuli detection task that targets extracting emotional regions that evoke people’s emotions (i.e., emotional stimuli) in artworks. This task offers new challenges to the community because of the diversity of artwork styles and the subjectivity of emotions, which can [...] Read more.
We introduce an emotional stimuli detection task that targets extracting emotional regions that evoke people’s emotions (i.e., emotional stimuli) in artworks. This task offers new challenges to the community because of the diversity of artwork styles and the subjectivity of emotions, which can be a suitable testbed for benchmarking the capability of the current neural networks to deal with human emotion. For this task, we construct a dataset called APOLO for quantifying emotional stimuli detection performance in artworks by crowd-sourcing pixel-level annotation of emotional stimuli. APOLO contains 6781 emotional stimuli in 4718 artworks for validation and testing. We also evaluate eight baseline methods, including a dedicated one, to show the difficulties of the task and the limitations of the current techniques through qualitative and quantitative experiments. Full article
(This article belongs to the Special Issue Methodologies and Applications of Image Understanding in Cultural and Artistic Heritage)
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