Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications

TL;DR

This paper introduces the Attributed Relational SIFT-based Regions Graph (ARSRG), a novel structure for digital image representation, and explores its theoretical foundation and practical adaptability across various applications.

Contribution

It provides the first formal definitions of ARSRG and investigates its structural properties and flexibility for diverse image content representations.

Findings

ARSRG can effectively encode different types of image content.

The structure demonstrates adaptability to various application scenarios.

Theoretical and experimental analyses confirm its flexibility and robustness.

Abstract

Graphs are widely adopted tools for encoding information. Generally, they are applied to disparate research fields where data needs to be represented in terms of local and spatial connections. In this context, a structure for ditigal image representation, called Attributed Relational SIFT-based Regions Graph (ARSRG), previously introduced, is presented. ARSRG has not been explored in detail in previous works and for this reason the goal is to investigate unknown aspects. The study is divided into two parts. A first, theoretical, introducing formal definitions, not yet specified previously, with purpose to clarify its structural configuration. A second, experimental, which provides fundamental elements about its adaptability and flexibility regarding different applications. The theoretical vision combined with the experimental one shows how the structure is adaptable to image representation including contents of different nature.