Name
Abstract | ||
|---|---|---|
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 © 2012 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1002/cplx.20388 | Complexity |
Keywords | Field | DocType |
organized complexity,wiley periodicals,information content,appropriate measure,inc. complexity,kolmogorov complexity,logical depth,physical complexity,physical world,image characterization,finer characterization,use result,information theory,computational complexity,image classification | Quantum complexity theory,Average-case complexity,Structural complexity theory,Kolmogorov complexity,Computer science,Theoretical computer science,Descriptive complexity theory,Complexity index,Artificial intelligence,Logical depth,Worst-case complexity,Machine learning | Journal |
Volume | Issue | ISSN |
17 | 3 | 1076-2787 |
Citations | PageRank | References |
9 | 0.80 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hector Zenil | 1 | 310 | 47.82 |
| Jean-Paul Delahaye | 2 | 325 | 54.60 |
| Cédric Gaucherel | 3 | 13 | 2.66 |