| Title |
Aesthetic Assessment Method of Advertising Design Images Based on Data Aggregation and Convolutional Neural Networks |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.2.163 |
| Keywords |
Advertisement; Aesthetic appreciation; Convolutional neural network; Data aggregation; Training; Evaluate |
| Abstract |
Traditional print advertisements are gradually receding from the historical stage of the advertising field because of their excessively homogeneous content and monotonous presentation. A method for aesthetically evaluating advertising design images based on convolutional neural networks is proposed in the study. This method utilizes two-way sub-networks that aim to extract and model the features of the images. One of the sub-networks in the path is the region of interest sub-network, which extracts features of the most appealing regions. The other sub-network is a multi-scale information sub-network engineered to provide a diverse range of global descriptive features. Additionally, this paper presents a training approach that utilizes data aggregation to establish an optimization direction for the model, focusing on the learning of concise samples while enhancing the model’s generalization ability through the integration of properly distributed sparse samples. Empirical analysis yields a classification accuracy of 85.79% and a mean aesthetic scoring error of 0.521. The model yielded aesthetic assessment results that closely matched those of the AVA dataset, with a goodness-of-fit score of 0.907. Consequently, it effectively evaluates advertisement design images and offers a reference point for designers, thus stimulating new ideas in the advertisement design industry. |