  In recent years research has been producing an important effort to encode the
digital image content. Most of the adopted paradigms only focus on local
features and lack in information about location and relationships between them.
To fill this gap, we propose a framework built on three cornerstones. First,
ARSRG (Attributed Relational SIFT (Scale-Invariant Feature Transform) regions
graph), for image representation, is adopted. Second, a graph embedding model,
with purpose to work in a simplified vector space, is applied. Finally, Fast
Graph Convolutional Networks perform classification phase on a graph based
dataset representation. The framework is evaluated on state of art object
recognition datasets through a wide experimental phase and is compared with
well-known competitors.
