[Paper] Packaging Design Analysis by Predicting User Preference and Semantic Attribute

抄録

Packaging design has a pronounced effect on consumer purchase behavior and can be a critical factor in marketing. Despite the importance, there are very few studies that have investigated optimal designs. In this work, in order to analyze packaging designs and support designing processes, we propose a deep learning based method with ensemble learning to predict user preference for packaging design. For qualitative analysis, we visualize the feature maps from the prediction model. Moreover, we predict the mentioned frequencies of semantic attributes in the questionnaires, which represent impressions that users have. The experimental results suggest that in terms of user preference prediction, the proposed model achieves a correlation coefficient of 0.652 between the ground truth user preference scores and the predicted values. As for the semantic attributes prediction, the highest correlation coefficient reaches 0.75. Also the visualization successfully indicates key elements in designs.