Jinyan Ouyang, Weitang Wang, Rui Zhang, Jianning Su, Shutao Zhang, Aimin Zhou
A framework for comprehensive aesthetic evaluation of product forms based on images is proposed, emphasizing interpretability and process automation. Starting from visual cognition and visual-organization principles, the method extracts shape-control cues from product images via segmentation and contour processing, and then operationalizes these cues as a set of aesthetic indices. The indices are integrated into a single score using grey relational analysis (GRA), where each weight has an explicit index-level meaning. We evaluate the framework on a car-front case study using large-scale online ratings. By comparing model outputs with large-scale online user ratings, the results indicate that the proposed approach can achieve efficient computation while maintaining a statistically significant positive association with large-scale online ratings. Overall, the framework links principle-oriented graphical descriptions with image-derived measurements, enabling automated assessment with interpretable index contributions and supporting broader applications in product-form design research.