On the Dynamism of Paintings Through the Distribution of Edge Directions
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 l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/10/11/276 |
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| author | Adrien Deliege Maria Giulia Dondero Enzo D’Armenio |
| author_facet | Adrien Deliege Maria Giulia Dondero Enzo D’Armenio |
| author_sort | Adrien Deliege |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e02ed3994c424729a02ccb7411a729f5 |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-e02ed3994c424729a02ccb7411a729f52025-08-20T01:54:02ZengMDPI AGJournal of Imaging2313-433X2024-11-01101127610.3390/jimaging10110276On the Dynamism of Paintings Through the Distribution of Edge DirectionsAdrien Deliege0Maria Giulia Dondero1Enzo D’Armenio2Department of Romance Languages and Literatures, Faculty of Philosophy and Letters, University of Liège, 4000 Liège, BelgiumDepartment of Romance Languages and Literatures, Faculty of Philosophy and Letters, University of Liège, 4000 Liège, BelgiumDepartment of Romance Languages and Literatures, Faculty of Philosophy and Letters, University of Liège, 4000 Liège, BelgiumThe 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.https://www.mdpi.com/2313-433X/10/11/276digital humanitiesedge detectioncomputer visionart analysis |
| spellingShingle | Adrien Deliege Maria Giulia Dondero Enzo D’Armenio On the Dynamism of Paintings Through the Distribution of Edge Directions Journal of Imaging digital humanities edge detection computer vision art analysis |
| title | On the Dynamism of Paintings Through the Distribution of Edge Directions |
| title_full | On the Dynamism of Paintings Through the Distribution of Edge Directions |
| title_fullStr | On the Dynamism of Paintings Through the Distribution of Edge Directions |
| title_full_unstemmed | On the Dynamism of Paintings Through the Distribution of Edge Directions |
| title_short | On the Dynamism of Paintings Through the Distribution of Edge Directions |
| title_sort | on the dynamism of paintings through the distribution of edge directions |
| topic | digital humanities edge detection computer vision art analysis |
| url | https://www.mdpi.com/2313-433X/10/11/276 |
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