Abstract

To investigate whether saliency modeling can be applied to the fashion area, this multidisciplinary study intended 1) to explore where consumers look at fashion advertisements using saliency-based models, and 2) to investigate which saliency model(s) are the most effective tools in predicting consumers’ visual attention when viewing fashion advertisements. Seventy college students were recruited to participate in the study at a mid-western university in the US. One hundred fashion images were selected from fashion magazines (e.g., Vogue) and divided into four image groups. Each participant was asked to view 25 fashion images and selected locations on each image that most attracted their attention by clicking a mouse on the screen. Ground-truth maps were created based on consumers’ attention. Six saliency models (AW5, COV, DeepFeat, GBVS, ISEEL, LDS) were applied to create saliency maps. The results of Ground-truth maps and saliency maps were compared, and further theoretical and practical implications were presented.

Highlights

  • Human beings tend to minimize their neural resources by using eye, head, and body movement to shift their visual attention and gaze behavior toward more informative image spatial locations (Mahdi et al, 2017)

  • It is meaningful to investigate whether saliency modeling can be applied to the fashion area

  • The results showed that all six saliency maps achieved a high agreement with the human attention data on fashion images of advertisements. It indicates that six saliency models can be used to predict where human subjects looked on the fashion advertisement images

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Summary

Introduction

Human beings tend to minimize their neural resources by using eye, head, and body movement to shift their visual attention and gaze behavior toward more informative image spatial locations (Mahdi et al, 2017). A saliency map is a 2D topological map that indicates visual attention priorities using a numerical scale. A diversity of saliency models has become a popular technical term for human vision attention study in fields such as communication and electronic engineering (e.g., Garnett et al, 2014; Mahdi et al, 2019; Tsiami et al, 2019).

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Conclusion

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