  Creativity and the understanding of cognitive processes involved in the
creative process are relevant to all of human activities. Comprehension of
creativity in the arts is of special interest due to the involvement of many
scientific and non scientific disciplines. Using digital representation of
paintings, we show that creative process in painting art may be objectively
recognized within the mathematical framework of self organization, a process
characteristic of nonlinear dynamic systems and occurring in natural and social
sciences. Unlike the artist identification process or the recognition of
forgery, which presupposes the knowledge of the original work, our method
requires no prior knowledge on the originality of the work of art. The original
paintings are recognized as realizations of the creative process which, in
general, is shown to correspond to self-organization of texture features which
determine the aesthetic complexity of the painting. The method consists of the
wavelet based statistical digital image processing and the measure of
statistical complexity which represents the minimal (average) information
necessary for optimal prediction. The statistical complexity is based on the
properly defined causal states with optimal predictive properties. Two
different time concepts related to the works of art are introduced: the
internal time and the artistic time. The internal time of the artwork is
determined by the span of causal dependencies between wavelet coefficients
while the artistic time refers to the internal time during which complexity
increases where complexity refers to compositional, aesthetic and structural
arrangement of texture features. The method is illustrated by recognizing the
original paintings from the copies made by the artists themselves, including
the works of the famous surrealist painter Ren\'{e} Magritte.
