Unlocking Visual Attraction: The Subtle Relationship between Image Features and Attractiveness

Zhoubao Sun, Kai Zhang, Yan Zhu, Yanzhe Ji and Pingping Wu ()
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Zhoubao Sun: School of Engineering Audit, Jiangsu Key Laboratory of Public Project Audit, Nanjing Audit University, Nanjing 211815, China
Kai Zhang: School of Computer Science, Jiangsu Key Laboratory of Public Project Audit, Nanjing Audit University, Nanjing 211815, China
Yan Zhu: School of Computer Science, Jiangsu Key Laboratory of Public Project Audit, Nanjing Audit University, Nanjing 211815, China
Yanzhe Ji: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Pingping Wu: School of Engineering Audit, Jiangsu Key Laboratory of Public Project Audit, Nanjing Audit University, Nanjing 211815, China

Mathematics, 2024, vol. 12, issue 7, 1-15

Abstract: The interest of advertising designers and operators in crafting appealing images is steadily increasing. With a primary focus on image attractiveness, this study endeavors to uncover the correlation between image features and attractiveness. The ultimate objective is to enhance the accuracy of predicting image attractiveness to achieve visually captivating effects. The experimental subjects encompass images sourced from the Shutterstock website, and the correlation between image features and attractiveness is analyzed through image attractiveness scores. In our experiments, we extracted traditional features such as color, shape, and texture from the images. Through a detailed analysis and comparison of the accuracy in predicting image attractiveness before and after feature selection using Lasso and LassoNet,, we confirmed that feature selection is an effective method for improving prediction accuracy. Subsequently, the Lasso and LassoNet feature selection methods were applied to a dataset containing image content features. The results verified an enhancement in the model’s accuracy for predicting image attractiveness with the inclusion of image content features. Finally, through an analysis of the four-dimensional features of color, texture, shape, and content, we identified specific features influencing image attractiveness, providing a robust reference for image design.

Keywords: attractiveness of images; feature selection; LassoNet; context features (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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