Poster graphic design with your Eyes: An approach to automatic textual layout design based on visual perception

•A visual saliency prediction model is obtained by fine-tuning training based on a graphic design dataset and probability density map obtained from a diffusion equation to guide the text layout of the graphic design.•Compared with the existing work, the text positions and sizes obtained by our method have higher correlations with the baseline, with the root mean square errors of the text position and size being reduced by an average of 30.95% and 14.03%, respectively.•The feasibility and effectiveness of this method are verified using a variety of reasonable evaluation methods (eye movement evaluation, user subjective evaluation and benchmark evaluation). The layout of graphic design can betedious, especially for non-professional users who require additional time. Therefore, this paper proposes a graphic design layout method based on visual perception. First, a graphic design dataset is trained based on a fully convolutional network, and a visual saliency prediction model is established to predict the visual saliency of the background image. Second, a probability density map of the background image is obtained using a diffusion equation. Third, based on a generation algorithm for a text anchor box, anchor boxes of different positions, sizes, and lengths are obtained. The experimental results show that, compared with previous work, the text position and size obtained by this method have higher correlations with the baseline.