Chen, Mei-YunYang, Ci-SyuanOuhyoung, MingJain, Eakta and Kosinka, Jirí2018-04-142018-04-1420181017-4656https://doi.org/10.2312/egp.20181008https://diglib.eg.org:443/handle/10.2312/egp20181008For novice painters, color mixing is a necessary skill which takes many years to learn. To get the skill easily, we design a system, a smart palette, to help them learn quickly. Our system is based on physical watercolor pigments, and we use a spectrometer to measure the transmittance and reflectance of watercolor pigments and collect a color mixing dataset. Moreover, we use deep neural network (DNN) to train a color mixing model. After that, using the model to predict a large amount of color mixing data creates a lookup table for color matching. In the smart palette, users can select a target color from an input image; then, the smart palette will find the nearest color, which is a matched color, and show a recipe where two pigments and their respective quantities can be mixed to get that color.Applied computingFine artsFine artsComputing methodologiesNeural networks10.2312/egp.201810081-2