Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Interactive Color Palette Tools
IEEE Computer Graphics and Applications
Crowdsourcing graphical perception: using mechanical turk to assess visualization design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data-driven image color theme enhancement
ACM SIGGRAPH Asia 2010 papers
Color compatibility from large datasets
ACM SIGGRAPH 2011 papers
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Color naming models for color selection, image editing and palette design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Probabilistic color-by-numbers: suggesting pattern colorizations using factor graphs
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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Color choice plays an important role in works of graphic art and design. However, it can be difficult to choose a compelling set of colors, or color theme, from scratch. In this work, we present a method for extracting color themes from images using a regression model trained on themes created by people. We collect 1600 themes from Mechanical Turk as well as from artists. We find that themes extracted by Turk participants were similar to ones extracted by artists. In addition, people tended to select diverse colors and focus on colors in salient image regions. We show that our model can match human-extracted themes more closely compared to previous work. Themes extracted by our model were also rated higher as representing the image than previous approaches in a Mechanical Turk study.