Neural network-based Chinese ink-painting art style learning

For purposes of intelligent art creation and existed painting style reusing, we presents a neural network-based Chinese ink-painting art style learning method, which is quite different from the traditional “pixel-wise” or “sample-wise” style transferring work. We first give a generalized definition for style features of Chinese ink painting, and then establish the style learning mechanisms with combination of back propagation neural network and image analysis techniques. The paralyzed global style features from input painting are analyzed by the well trained style learning system, the learning outputs are extracted from style information library for Chinese painting. The experiment results show that the method works well, and it is obviously a new exploration for painting style learning.