Predicting Visual Importance Across Graphic Design Types
| Autor: | Vincent Casser, Peter O'Donovan, Camilo Fosco, Aaron Hertzmann, Amish Kumar Bedi, Zoya Bylinskii |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Generalization Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Computer Science - Human-Computer Interaction Design elements and principles 02 engineering and technology Machine learning computer.software_genre Image (mathematics) Human-Computer Interaction (cs.HC) Computer Science - Graphics 0202 electrical engineering electronic engineering information engineering FOS: Electrical engineering electronic engineering information engineering Natural (music) 0501 psychology and cognitive sciences 050107 human factors business.industry Deep learning 05 social sciences Infographic Image and Video Processing (eess.IV) 020207 software engineering Unified Model Graphic design Electrical Engineering and Systems Science - Image and Video Processing Graphics (cs.GR) Artificial intelligence business computer |
| Zdroj: | UIST |
| Popis: | This paper introduces a Unified Model of Saliency and Importance (UMSI), which learns to predict visual importance in input graphic designs, and saliency in natural images, along with a new dataset and applications. Previous methods for predicting saliency or visual importance are trained individually on specialized datasets, making them limited in application and leading to poor generalization on novel image classes, while requiring a user to know which model to apply to which input. UMSI is a deep learning-based model simultaneously trained on images from different design classes, including posters, infographics, mobile UIs, as well as natural images, and includes an automatic classification module to classify the input. This allows the model to work more effectively without requiring a user to label the input. We also introduce Imp1k, a new dataset of designs annotated with importance information. We demonstrate two new design interfaces that use importance prediction, including a tool for adjusting the relative importance of design elements, and a tool for reflowing designs to new aspect ratios while preserving visual importance. The model, code, and importance dataset are available at https://predimportance.mit.edu . |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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