  Learning color mixing is difficult for novice painters. In order to support
novice painters in learning color mixing, we propose a prediction model for
semitransparent pigment mixtures and use its prediction results to create a
Smart Palette system. Such a system is constructed by first building a
watercolor dataset with two types of color mixing data, indicated by
transmittance and reflectance: incrementation of the same primary pigment and a
mixture of two different pigments. Next, we apply the collected data to a deep
neural network to train a model for predicting the results of semitransparent
pigment mixtures. Finally, we constructed a Smart Palette that provides
easily-followable instructions on mixing a target color with two primary
pigments in real life: when users pick a pixel, an RGB color, from an image,
the system returns its mixing recipe which indicates the two primary pigments
being used and their quantities.
