A quantitative model for identifying regions of design visual attraction and application to automobile styling

Analysis of design regions of visual attention that affect aesthetic appeal is an important topic for both practicing designers and design researchers. The paper introduces a data-driven methodology consisting of four stages: (1) design feature learning, (2) design attribute prediction, (3) salient feature selection, and (4) salient feature visualization. Using this methodology, we making inroads to inverting the nonlinear function from design images and design aesthetic attributes, and give preliminary results for an automotive styling study.

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