PREDICTING VISUAL APPEAL IN ADVERTISING PHOTOGRAPHY
DOI:
https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6836Keywords:
Visual Aesthetics Prediction, Advertising Photography, Deep Learning Models, Transformer-Based Feature Analysis, Computational AestheticsAbstract [English]
Digital advertising has grown at a very high rate, supporting the necessity of images that are visually appealing and able to attract the consumer on the perception of the image and motivation to purchase a product. The study is a predictive model of visual appeal in advertising photography based on the methods of computational aesthetics, machine learning, and deep learning. The study conceptualizes, in the first instance, visual appeal as a construct of multidimensions that is influenced by composition, lighting, color harmony, subject prominence, emotional tone, and style. A large data set of advertising photos, gathered in several products and media are labeled by a structured labeling protocol, which measures aesthetic quality that is perceived by humans. They use both handcrafted and deep visual descriptors, obtained with the help of pretrained convolutional neural networks and transformer-based encoders, to construct predictive models. Its methodology consists of a preprocessing and normalization pipeline as a whole and two large families of models, CNNs to learn spatial features and transformers to learn contextual features worldwide. Empirical evidence shows that deep representations perform better than handcrafted features at fine-grain aesthetics and that transformer models are more able to predict the associations between the visual complexity and the scores of visual attractiveness. Another limitation noted in the study is the subjectivity of the datasets, cultural biasness, and lack of diversity in advertising situations.
References
Bouwman, E. P., Bolderdijk, J. W., Onwezen, M. C., and Taufik, D. (2022). “Do you Consider Animal Welfare to be Important?” Activating Cognitive Dissonance Via Value Activation Can Promote Vegetarian Choices. Journal of Environmental Psychology, 83, 101871. https://doi.org/10.1016/j.jenvp.2022.101871
Chan, E. Y., and Septianto, F. (2024). Self-Construals and Health Communications: The Persuasive Roles of Guilt and Shame. Journal of Business Research, 170, 114357. https://doi.org/10.1016/j.jbusres.2023.114357
Fang, J., Wen, Z., and He, Z. (2023). Moderated Mediation Model Analysis of Common Categorical Variables. Applied Psychology, 29, 291–299.
Fechner, D., and Isbanner, S. (2025). Understanding the Intention–Behaviour Gap in Meat Reduction: The Role of Cognitive Dissonance in Dietary Change. Appetite, 214, 108204. https://doi.org/10.1016/j.appet.2025.108204
Gradidge, S., Zawisza, M., Harvey, A. J., and McDermott, D. T. (2021). A Structured Literature Review of the Meat Paradox. Social Psychological Bulletin, 16, e5953. https://doi.org/10.32872/spb.5953
Guo, L. (2021). Application of Animal Images in Food Packaging Design: Taking Traditional Tibetan Auspicious Patterns as an Example. Green Packaging, 6, 96–99.
Hurst, K. F., and Sintov, N. D. (2022). Guilt Consistently Motivates Pro-Environmental Outcomes While Pride Depends on Context. Journal of Environmental Psychology, 80, 101776. https://doi.org/10.1016/j.jenvp.2022.101776
Ioannidou, M., Lesk, V., Stewart-Knox, B., and Francis, K. B. (2023). Moral Emotions and Justifying Beliefs about Meat, Fish, Dairy and Egg Consumption: A Comparative Study of Dietary Groups. Appetite, 186, 106544. https://doi.org/10.1016/j.appet.2023.106544
Jacobs, T. P., Wang, M., Leach, S., Siu, H. L., Khanna, M., Chan, K. W., Chau, H. T., Tam, K. Y. Y., and Feldman, G. (2024). Revisiting the Motivated Denial of Mind to Animals Used for Food: Replication Registered Report of Bastian et al. (2012). International Review of Social Psychology, 37, 6. https://doi.org/10.5334/irsp.932
Jiang, L. A., Feng, Y., Zhou, W., Yang, Z., and Su, X. (2024). Too Anthropomorphized to Keep Distance: The Role of Social Psychological Distance on Meat Inclinations. Appetite, 196, 107272. https://doi.org/10.1016/j.appet.2024.107272
Kim, D. J. M., and Yoon, S. (2021). Guilt of the Meat-Eating Consumer: When Animal Anthropomorphism Leads to Healthy Meat Dish Choices. Journal of Consumer Psychology, 31, 665–683. https://doi.org/10.1002/jcpy.1215
Nielsen, R. S., Gamborg, C., and Lund, T. B. (2024). Eco-guilt and Eco-Shame in Everyday Life: An Exploratory Study of the Experiences, Triggers, and Reactions. Frontiers in Sustainability, 5, 1357656. https://doi.org/10.3389/frsus.2024.1357656
Ramdani, M. A., and Belgiawan, P. F. (2023). Designing Instagram Advertisement Content: What Design Elements Influence Customer Attitude and Purchase Behavior? Contemporary Management Research, 19, 1–26. https://doi.org/10.7903/cmr.23023
Sheng, G., Xia, Q., and Yue, B. (2020). Effectiveness of Green Advertising from the Perspective of Image Proximity. Xinwen Yu Chuanbo Pinglun, 73, 59–69.
Wang, Z., and Park, J. (2023). “Human-like” is Powerful: The Effect of Anthropomorphism on Psychological Closeness and Purchase Intention in Insect Food Marketing. Food Quality and Preference, 109, 104901. https://doi.org/10.1016/j.foodqual.2023.104901
Yu, H. (2022). Application of Animal Anthropomorphic Images Mixed with Graffiti Style in Packaging Design. Xin Mei Yu, 10, 99–101. https://doi.org/10.18282/l-e.v10i5.2687
Zhang, Y., and Huang, S. (2024). The Influence of Visual Marketing on Consumers’ Purchase Intention of Fast Fashion Brands in China: An Exploration Based on the fsQCA Method. Frontiers in Psychology, 15, 1190571. https://doi.org/10.3389/fpsyg.2024.1190571
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dr. S L Jany Shabu, Rajat Saini, Dr. Ashwini Kumar, Pooja Yadav, Shweta Ishwar Gadave, Dr. Sasmeeta Tripathy
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
