Abstract
Contrary to natural images with randomized content, advertisements contain abundant emotion-eliciting manufactured scenes and multi-modal visual elements with highly related semantics. However, little research has evaluated the interrelationships of advertising vision and affective perception. The absence of advertising data sets with affective labels and visual attention benchmarks is one of the most pressing issues that have to be addressed. Meanwhile, growing evidence indicates that eye movements can reveal the internal states of human minds. Inspired by these, we use a high-precision eye tracker to record the eye-moving data of 57 subjects when they observe 1000 advertising images. 7-score opinion ratings for the five advertising attributes (i.e., ad liking, emotional, aesthetic, functional, and brand liking) are then collected. We further make a preliminary analysis of the correlation among advertising attributes, subjects’ visual attention, eye movement characteristics, and personality traits, obtaining a series of enlightening conclusions. To our best knowledge, the proposed dataset is the largest advertising image dataset based on eye tracking and with multiple personalized affective tags. It provides a new exploration space and data foundation for multimedia visual analysis and affection computing community. The data are available at: https://github.com/lscumt/EAID.
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Liang, S., Liu, R., Qian, J. (2024). EAID: An Eye-Tracking Based Advertising Image Dataset with Personalized Affective Tags. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14495. Springer, Cham. https://doi.org/10.1007/978-3-031-50069-5_24
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