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README.md

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+# Image Charaterization through Estimations of Algorithmic Information (Kolmogorov Complexity)
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+A layered version of the Block Decomposition Method[1], serves as a descriptor of both weighted networks and grayscale or color images.  This descriptor provides an estimate of Kolmogorov Complexity that's sensitive to morphological perturbative [2].  To estimate the complexity of a grayscale texture, we quantize it and aggregate the estimated Kolmogorov complexity values of binary 4 x 4 squares, estimated through the Coding Theorem Method [3, 4].
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+### References
+[1] Hector Zenil, Santiago Hernández - Orozco, Narsis A.Kiani, Fernando Soler - Toscano, Antonio Rueda - Toicen, and Jesper Tegner "A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic Complexity", https://arxiv.org/abs/1609.00110
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+[2] Antonio Rueda-Toicen, Narsis A. Kiani, and Hector Zenil, "Morphological Image Analysis through Estimations of Kolmogorov Complexity" (in preparation)
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+[3] Fernando Soler - Toscano, Hector Zenil, Jean-Paul Delahaye, and Nicolas Gauvrit (2014) "Calculating Kolmogorov Complexity from the Output Frequency Distributions of Small Turing Machines." PLoS ONE 9 (5) : e96223.
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+[4] Hector Zenil, Fernando Soler - Toscano, K. Dingle.and Aard Louis (2014) "Correlation of Automorphism Group Size and Topological Properties with Program-size Complexity Evaluations of Graphs and Complex Networks", Physica A : Statistical Mechanics and its Applications, vol.404, pp.341\[Dash]358. 
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+### Author: Antonio Rueda-Toicen
+antonio "dot" rueda "." toicen "at" gmail 'dot' com