  The artistic community is increasingly relying on automatic computational
analysis for authentication and classification of artistic paintings. In this
paper, we identify hidden patterns and relationships present in artistic
paintings by analysing their complexity, a measure that quantifies the sum of
characteristics of an object. Specifically, we apply Normalized Compression
(NC) and the Block Decomposition Method (BDM) to a dataset of 4,266 paintings
from 91 authors and examine the potential of these information-based measures
as descriptors of artistic paintings. Both measures consistently described the
equivalent types of paintings, authors, and artistic movements. Moreover,
combining the NC with a measure of the roughness of the paintings creates an
efficient stylistic descriptor. Furthermore, by quantifying the local
information of each painting, we define a fingerprint that describes critical
information regarding the artists' style, their artistic influences, and shared
techniques. More fundamentally, this information describes how each author
typically composes and distributes the elements across the canvas and,
therefore, how their work is perceived. Finally, we demonstrate that regional
complexity and two-point height difference correlation function are useful
auxiliary features that improve current methodologies in style and author
classification of artistic paintings. The whole study is supported by an
extensive website (http://panther.web.ua.pt) for fast author characterization
and authentication.
