一种新的模糊属性图匹配相似度度量方法及其在手写体字符识别中的应用

Proceedings 1992 International Carnahan Conference on Security Technology: Crime Countermeasures Pub Date : 1992-10-14 DOI:10.1109/CCST.1992.253754

G. Man, J. Poon

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引用次数: 5

摘要

在模式识别和分类过程中，将模式映射到特征空间时，通常使用模糊概念。为了处理这类模糊概念，作者通过模糊属性，将属性图扩展为模糊属性图(FAG)。属性图被广泛用作结构模式的直接表示。图形的顶点表示描述模式的模式原语，而弧是这些原语之间的关系。给出了聚类图的形式化定义，并引入了聚类图相似度度量的新方法来解决图匹配问题。开发了一个手写数字识别器来评估所提出算法的有效性，在正确分类方面，它的准确率达到93%。

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A new similarity measurement method for fuzzy-attribute graph matching and its application to handwritten character recognition

For problems of pattern recognition and classification procedures, the concept of fuzziness is usually applied when patterns are mapped into the feature space. To handle such fuzzy concepts, the authors extend the attributed graph to the fuzzy-attribute graph (FAG) by making the attributes fuzzy. The attributed graph is widely used as a straightforward representation of structural patterns. The vertices of the graph represent pattern primitives describing the pattern while the arc is the relation between these primitives. The authors give a formal definition of FAGs and introduce a new method of similarity measurement for FAGs to solve the problem of graph matching. A handwritten numeral recognizer was developed to evaluate the effectiveness of the proposed algorithm, and it gave an accuracy of 93% in terms of correct classification.<>

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Proceedings 1992 International Carnahan Conference on Security Technology: Crime Countermeasures

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