  This work presents a method to decompose a convolutional layer of the deep
neural network into painting actions. To behave like the human painter, these
actions are driven by the cost simulating the hand movement, the paint color
change, the stroke shape and the stroking style. To help planning, the Mask
R-CNN is applied to detect the object areas and decide the painting order. The
proposed painting system introduces a variety of extensions in artistic styles,
based on the chosen parameters. Further experiments are performed to evaluate
the channel penetration and the channel sensitivity on the strokes.
