Eye-tracking technology has gained prominence in cultural heritage studies, facilitating behavioral analysis and visitor engagement assessments. This paper explores the challenges and future directions of artwork segmentation in eye-tracking experiments, aiming to automate the identification of areas of interest. Although existing segmentation approaches, such as semantic segmentation models, show promise, they face limitations in accurately segmenting diverse artwork styles. We propose hybrid segmentation as a viable strategy, combining multiple techniques for improved accuracy. Through qualitative analysis, we evaluate segmentation models on public domain artworks, highlighting the strengths and weaknesses of each approach.

Ferrato, A., Limongelli, C., Mezzini, M., Sansonetti, G., Micarelli, A. (2024). Artwork Segmentation in Eye-Tracking Experiments: Challenges and Future Directions. In UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp.477-481). New York, NY : Association for Computing Machinery, Inc [10.1145/3631700.3664906].

Artwork Segmentation in Eye-Tracking Experiments: Challenges and Future Directions

Ferrato, Alessio;Limongelli, Carla;Mezzini, Mauro;Sansonetti, Giuseppe
;
Micarelli, Alessandro
2024-01-01

Abstract

Eye-tracking technology has gained prominence in cultural heritage studies, facilitating behavioral analysis and visitor engagement assessments. This paper explores the challenges and future directions of artwork segmentation in eye-tracking experiments, aiming to automate the identification of areas of interest. Although existing segmentation approaches, such as semantic segmentation models, show promise, they face limitations in accurately segmenting diverse artwork styles. We propose hybrid segmentation as a viable strategy, combining multiple techniques for improved accuracy. Through qualitative analysis, we evaluate segmentation models on public domain artworks, highlighting the strengths and weaknesses of each approach.
2024
Ferrato, A., Limongelli, C., Mezzini, M., Sansonetti, G., Micarelli, A. (2024). Artwork Segmentation in Eye-Tracking Experiments: Challenges and Future Directions. In UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp.477-481). New York, NY : Association for Computing Machinery, Inc [10.1145/3631700.3664906].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/491234
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