G.A. Bilodeau and R. Bergevin
Part graph, structural indexing, fuzzy attributes, graph matching, object model
In object recognition and image-querying applications, complex
graphs often have to be compared to verify the similarity between
two models. As there is always uncertainty while models are
constructed, the nodes and the edges require fuzzy attributes to
properly describe the scene or the object. This work addresses the
problem of matching graphs with fuzzy attributes (GFAs) obtained
by hypothesizing volumetric primitives from 2D parts. The GFAs
of interests have nodes with many fuzzy attributes that correspond
to volumetric primitive hypotheses, and edges that describe the
spatial relationship between the hypothesized volumetric primitives.
A model for representing 2D parts by volumetric primitives is
presented. Then, a method using redeﬁned structural indexing
adapted to GFAs is proposed. This inexact matching method has
been designed for matching GFAs in large databases.