Examination of the relation between affective content of images and gaze behavior

Saliency maps show the likelihood of each pixel on the image being looked at. They are often computed considering a neutral human subject, who is considered to be simply observing the image without any particular motivation (e.g, searching, registering etc.) or feelings (e.g, excited, fearful etc.)....

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Bibliographic Details
Published in2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter) pp. 101 - 107
Main Authors Kasahara, Terumi, Supitayakul, Parisa, Yucel, Zeynep, Monden, Akito
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.12.2022
Subjects
Affective computing
Behavioral sciences
circumplex model
Computational modeling
emotions
eye gaze
Informatics
Measurement
Registers
saliency
Task analysis
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Summary:Saliency maps show the likelihood of each pixel on the image being looked at. They are often computed considering a neutral human subject, who is considered to be simply observing the image without any particular motivation (e.g, searching, registering etc.) or feelings (e.g, excited, fearful etc.). In this study, we focus on the emotional aspect and investigate whether there is any need to adjust these saliency maps to account for the emotions that they may induce in the viewers. To that end, we choose a set of images from an emotional image data set and display them to human subjects. We register their eye gaze and compute how well empirical gaze data matches the saliency maps. We quantify this based on two distribution-based (AUC, IG) and two location-based saliency metrics (CC and SIM). We see that although there are some parallels in some of these metrics, the evidence is not strong enough to claim a significant contribution due to affective content.
DOI:10.1109/IIAI-AAI-Winter58034.2022.00030