Visibility graphs for image processing
This work introduces a novel graph-based approach for image analysis, potentially benefiting researchers in computer vision and pattern recognition.
The authors explored the use of image visibility graphs (IVGs) for image processing and classification, demonstrating that their link architecture captures structural information and introducing features like Visibility Patches that are informative, efficient, and applicable for pattern recognition tasks.
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.