Summary
In graphical pattern recognition, each data is represented as an arrangement of elements, that encodes both the properties of each element and the relations among them. Hence, patterns are modelled as labelled graphs where, in general, labels can be attached to both nodes and edges. Artificial neural networks able to process graphs are a powerful tool for addressing a great variety of real-world problems, where the information is naturally organized in entities and relationships among entities and, in fact, they have been widely used in computer vision, f.i. in logo recognition, in similarity retrieval, and for object detection. In this chapter, we propose a survey of neural network models able to process structured information, with a particular focus on those architectures tailored to address image understanding applications. Starting from the original recursive model (RNNs), we subsequently present different ways to represent images – by trees, forests of trees, multiresolution trees, directed acyclic graphs with labelled edges, general graphs – and, correspondingly, neural network architectures appropriate to process such structures.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag US
About this chapter
Cite this chapter
Bianchini, M., Scarselli, F. (2009). Artificial Neural Networks for Processing Graphs with Application to Image Understanding: A Survey. In: Jeong, J., Damiani, E. (eds) Multimedia Techniques for Device and Ambient Intelligence. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-88777-7_8
Download citation
DOI: https://doi.org/10.1007/978-0-387-88777-7_8
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-88776-0
Online ISBN: 978-0-387-88777-7
eBook Packages: Computer ScienceComputer Science (R0)