MATCHING GRAPHS WITH FUZZY ATTRIBUTES IN MACHINE VISION Abstract

In object recognition and image querying applications, complex graphs often have to be compared to verify the similarity between two models. Since 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 paper 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 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 structural indexing adapted to GFAs is proposed. This inexact matching method has been designed for matching GFAs in large databases