Function-Described Graphs for Structural Pattern Recognition
This provides a novel structural pattern recognition method for computer vision tasks like 3D-object and face recognition, though it appears incremental relative to existing graph-based approaches.
The authors introduced Function-Described Graphs (FDGs) as a compact probabilistic model for sets of attributed graphs, enabling structural pattern recognition with defined distance measures and an efficient matching algorithm. They demonstrated applications in 3D-object modeling and human face recognition using multiple views.
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.