Function-Described Graphs for Structural Pattern Recognition

TL;DR

This paper introduces Function-described graphs (FDGs), a compact probabilistic model for representing and recognizing attributed graphs, with applications in 3D object matching and human face recognition.

Contribution

The paper proposes FDGs as a novel probabilistic graph model, defines their features and distance measures, and demonstrates their effectiveness in pattern recognition tasks.

Findings

FDGs effectively model multi-view 3D objects.

FDGs successfully recognize human faces from multiple views.

The matching algorithm is efficient and accurate.

Abstract

We present in this article the model Function-described graph (FDG), which is a type of compact representation of a set of attributed graphs (AGs) that borrow from Random Graphs the capability of probabilistic modelling of structural and attribute information. We define the FDGs, their features and two distance measures between AGs (unclassified patterns) and FDGs (models or classes) and we also explain an efficient matching algorithm. Two applications of FDGs are presented: in the former, FDGs are used for modelling and matching 3D-objects described by multiple views, whereas in the latter, they are used for representing and recognising human faces, described also by several views.