Graphs are a powerful and popular representation formalism in pattern recognition.
Particularly in the field of document analysis they have found widespread application. From …

The field of pattern recognition is usually subdivided into the statistical and the structural
approach. Structural pattern recognition allows one to use powerful and flexible …

In this paper, we examine the main advances registered in the last ten years in Pattern
Recognition methodologies based on graph matching and related techniques, analyzing …

This book is concerned with a fundamentally novel approach to graph-based pattern
recognition based on vector space embedding of graphs. It aims at condensing the high …

Graph edit distance is a powerful and flexible method for error-tolerant graph matching. Yet it
can only be calculated for small graphs in practice due to its exponential time complexity …

This paper presents a survey as well as an empirical comparison and evaluation of seven
kernels on graphs and two related similarity matrices, that we globally refer to as “kernels on …

This paper is a historical overview of graph-based methodologies in Pattern Recognition in
the last 40 years; history is interpreted with the aim of recognizing the rationale inspiring the …

This paper proposes a new Quantum Spatial Graph Convolutional Neural Network
(QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes …

In this paper, we develop a novel backtrackless aligned-spatial graph convolutional network
(BASGCN) model to learn effective features for graph classification. Our idea is to transform …

Graph structures have been proved important in high level-vision since they can be used to
represent structural and relational arrangements of objects in a scene. One of the problems …