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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.