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Aug. 2014 (vol. 20 no. 8)
pp. 1200-1213
Peter ODonovan, Department of Computer Science , University of Toronto, Bahen Centre, 40 St. George Street, Toronto, Canada
Aseem Agarwala, Adobe Research, Adobe Systems Inc., Seattle,
Aaron Hertzmann, Department of Computer Science , University of Toronto, 10 King’s College Rd, Rm 3302, Toronto, Canada
This paper presents an approach for automatically creating graphic design layouts using a new energy-based model derived from design principles. The model includes several new algorithms for analyzing graphic designs, including the prediction of perceived importance, alignment detection, and hierarchical segmentation. Given the model, we use optimization to synthesize new layouts for a variety of single-page graphic designs. Model parameters are learned with Nonlinear Inverse Optimization (NIO) from a small number of example layouts. To demonstrate our approach, we show results for applications including generating design layouts in various styles, retargeting designs to new sizes, and improving existing designs. We also compare our automatic results with designs created using crowdsourcing and show that our approach performs slightly better than novice designers.
Index Terms:
Layout,Computational modeling,Optimization,Predictive models,Algorithm design and analysis,Face,nonlinear inverse optimization,Graphic design,layout,modeling,learning,crowdsourcing
Citation:
Peter ODonovan, Aseem Agarwala, Aaron Hertzmann, "Learning Layouts for Single-PageGraphic Designs," IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 8, pp. 1200-1213, Aug. 2014, doi:10.1109/TVCG.2014.48
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