Image Characterization and Classification by Physical Complexity

TL;DR

This paper introduces a novel method for estimating image complexity using Bennett's logical depth, offering a finer classification of images based on their organized information content.

Contribution

The paper proposes the first implementation of a classification method for images based on logical depth, providing a new approach to evaluate visual complexity.

Findings

Effective classification of images by logical depth

Provides a finer measure of complexity than Kolmogorov complexity

First application of logical depth in image analysis

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.