2 research outputs found
Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications
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.Comment: 28 pages, 7 figures, submitted to Journal of Artificial Intelligence
Research (https://www.jair.org/
FastGCN+ARSRGemb: a novel framework for object recognition
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