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Complexity
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Image characterization and classification by physical complexity

Authors: Zenil, Hector; Gaucherel, Cédric; Delahaye, Jean-Paul;

Image characterization and classification by physical complexity

Abstract

AbstractWe 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

Countries
France, United Kingdom
Keywords

FOS: Computer and information sciences, Image classification, Computer Science - Information Theory, Information Theory (cs.IT), Computational Complexity (cs.CC), Algorithmic randomness, 004, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, Computer Science - Computational Complexity, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Bennett's logical depth, Information content, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Algorithmic complexity

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
26
Top 10%
Top 10%
Top 10%
Green
gold