Using Fuzzy Modifier in Similarity Measure of Fuzzy Attribute Graph and Its Automatic Selection in Structural Pattern Recognition

Abstract

Fuzzy Attribute Graph (FAG) is a powerful tool for representation and recognition of structural patterns. The conventional framework for similarity measure of FAGs is based on equivalent fuzzy attributes but in fuzzy world, some attributes are more important. In this paper, a modified recognition framework, using linguistic modifier for matching of the fuzzy attribute graphs, is introduced. Then an algorithm for automatic selection of fuzzy modifier based on the learning patterns is posed. Some examples for the conventional and modified framework for FAG similarity measure are studied and the potential of the proposed framework for FAG matching is showed.