Product Design Award Prediction Modeling: Design Visual Aesthetic Quality Assessment via DCNNs

Abstract

A visual aesthetic is a crucial determinant of product design evaluation. Through the analysis of image features, not only can we evaluate the aesthetic level, but also we can reveal the whole quality of the design proposal. We assume that it could be a potential pattern to predict the ultimate success of the proposal in product design that a visual aesthetic can be a cue for award classification modeling. Consequently, we conduct investigation on a dataset of over 10,003 design submissions in a design competition held once a year from 2008 to 2018 in order to manifest the assumption. Due to the remarkable performance of deep convolutional neural networks (DCNNs), we compare seven deep learning methods to explore an optimal model for design award prediction based on product image analysis. The result of the experiments indicates that the proposed method achieves comparative accuracy in design award classification result predication, with the optimal classification accuracy of 70.79% using the SEFL-ResNet (Squeeze and Excitation - Focal Loss - ResNet) method.