24 research outputs found
Learning Visual Importance for Graphic Designs and Data Visualizations
Knowing where people look and click on visual designs can provide clues about
how the designs are perceived, and where the most important or relevant content
lies. The most important content of a visual design can be used for effective
summarization or to facilitate retrieval from a database. We present automated
models that predict the relative importance of different elements in data
visualizations and graphic designs. Our models are neural networks trained on
human clicks and importance annotations on hundreds of designs. We collected a
new dataset of crowdsourced importance, and analyzed the predictions of our
models with respect to ground truth importance and human eye movements. We
demonstrate how such predictions of importance can be used for automatic design
retargeting and thumbnailing. User studies with hundreds of MTurk participants
validate that, with limited post-processing, our importance-driven applications
are on par with, or outperform, current state-of-the-art methods, including
natural image saliency. We also provide a demonstration of how our importance
predictions can be built into interactive design tools to offer immediate
feedback during the design process
Realistic Saliency Guided Image Enhancement
Common editing operations performed by professional photographers include the
cleanup operations: de-emphasizing distracting elements and enhancing subjects.
These edits are challenging, requiring a delicate balance between manipulating
the viewer's attention while maintaining photo realism. While recent approaches
can boast successful examples of attention attenuation or amplification, most
of them also suffer from frequent unrealistic edits. We propose a realism loss
for saliency-guided image enhancement to maintain high realism across varying
image types, while attenuating distractors and amplifying objects of interest.
Evaluations with professional photographers confirm that we achieve the dual
objective of realism and effectiveness, and outperform the recent approaches on
their own datasets, while requiring a smaller memory footprint and runtime. We
thus offer a viable solution for automating image enhancement and photo cleanup
operations.Comment: For more info visit http://yaksoy.github.io/realisticEditing