Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications
This work addresses the need for flexible image representation in computer vision, but it is incremental as it builds on an existing structure without major breakthroughs.
The paper tackles the problem of representing digital images using a graph structure called ARSRG, which had not been previously explored in detail, by providing formal definitions and experimental validation to demonstrate its adaptability to diverse image contents.
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.