Kolmogorov compression complexity may differentiate different schools of Orthodox iconography

Abstract

The complexity in the styles of 1200 Byzantine icons painted between 13th and 16th from Greece, Russia and Romania was investigated through the Kolmogorov algorithmic information theory. The aim was to identify specific quantitative patterns which define the key characteristics of the three different painting schools. Our novel approach using the artificial surface images generated with Inverse FFT and the Midpoint Displacement (MD) algorithms, was validated by comparison of results with eight fractal and non-fractal indices. From the analyzes performed, normalized Kolmogorov compression complexity (KC) proved to be the best solution because it had the best complexity pattern differentiations, is not sensitive to the image size and the least affected by noise. We conclude that normalized KC methodology does offer capability to differentiate the icons within a School and amongst the three Schools.

Fractal algorithms and RGB image processing in scribal and ink identification on an 1819 secret initiation manuscript to the “Philike Hetaereia”

“…Further application of the box-counting fractal analysis method for determining the compaction pattern of forest areas 24 and analysis of the fragmentation tendency of forest areas in of Romania [25][26][27][28][29] have been published. Kinetics and dynamics of animal behavior 30 , genome organization 31 , neuroscience 32 ), works of art 33 and many other examples highlight the presence of Euclidean forms and dynamics.…”