  Neural style transfer has been demonstrated to be powerful in creating
artistic image with help of Convolutional Neural Networks (CNN). However, there
is still lack of computational analysis of perceptual components of the
artistic style. Different from some early attempts which studied the style by
some pre-processing or post-processing techniques, we investigate the
characteristics of the style systematically based on feature map produced by
CNN. First, we computationally decompose the style into basic elements using
not only spectrum based methods including Fast Fourier Transform (FFT),
Discrete Cosine Transform (DCT) but also latent variable models such Principal
Component Analysis (PCA), Independent Component Analysis (ICA). Then, the
decomposition of style induces various ways of controlling the style elements
which could be embedded as modules in state-of-the-art style transfer
algorithms. Such decomposition of style brings several advantages. It enables
the computational coding of different artistic styles by our style basis with
similar styles clustering together, and thus it facilitates the mixing or
intervention of styles based on the style basis from more than one styles so
that compound style or new style could be generated to produce styled images.
Experiments demonstrate the effectiveness of our method on not only painting
style transfer but also sketch style transfer which indicates possible
applications on picture-to-sketch problems.
