Open AccessImage characterization and classification by physical complexity
Author(s) -
Zenil Hector,
Delahaye JeanPaul,
Gaucherel Cédric
Publication year - 2011
Publication title -
complexity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 61
eISSN - 1099-0526
pISSN - 1076-2787
DOI - 10.1002/cplx.20388
Subject(s) - kolmogorov complexity , computer science , measure (data warehouse) , image (mathematics) , characterization (materials science) , computational complexity theory , artificial intelligence , logical analysis , logical conjunction , descriptive complexity theory , worst case complexity , time complexity , theoretical computer science , pattern recognition (psychology) , machine learning , algorithm , data mining , mathematics , mathematical statistics , statistics , materials science , programming language , nanotechnology
We present a method for estimating the complexity of an image based on Bennett's concept of logical depth. Bennett identified logical depth as the appropriate measure of organized complexity, and hence as being better suited to the evaluation of the complexity of objects in the physical world. Its use results in a different, and in some sense a finer characterization than is obtained through the application of the concept of Kolmogorov complexity alone. We use this measure to classify images by their information content. The method provides a means for classifying and evaluating the complexity of objects by way of their visual representations. To the authors' knowledge, the method and application inspired by the concept of logical depth presented herein are being proposed and implemented for the first time. © 2011 Wiley Periodicals, Inc. Complexity, 2011