Visibility graphs for image processing

TL;DR

This paper investigates the application of image visibility graphs (IVGs) in image processing and classification, demonstrating their effectiveness as filters, compressors, and pattern recognition tools through novel graph features.

Contribution

The paper introduces the use of IVGs for image analysis, including new features like Visibility Patches, and shows their utility in classification and image filtering.

Findings

IVGs encode relevant image structure information

Visibility Patches are highly informative features

IVGs are computationally efficient and universally applicable

Abstract

The family of image visibility graphs (IVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters and compressors. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classification tasks.