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Symbol Recognition Using a Galois Lattice of Frequent Graphical Patterns

机译:使用常见的图形模式的Galois格子符号识别

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Graphics recognition is an important task in many real-life applications. In this article, we propose a new approach to recognize graphical symbols by the use of a frequent Galois lattice. We propose to build a concept lattice not in terms of graphical patterns but in terms of frequent graphical patterns. The purpose of this paper is twofold : first, we try to identify the best primitives from a given graphical symbol based on a descriptor invariant to rotation, translation and scaling. Each symbol is decribed using a feature vector computed on stable neighborhood for a set of points chosen randomly from the symbol. Secondly, we propose a new recognition approach based on a frequent Galois lattice. The obtained concept lattice based on frequent patterns is used as a classifier. The retrieval performance and behavior of the method have been tested for graphics recognition. We have compared our method with others based on different descriptors and classifiers. Our approach proves that the symbol description method and the algorithm used to extract frequent attributes to build the frequent Galois lattice are suitable to the recognition process.
机译:图形识别是许多现实生活中的重要任务。在本文中,我们提出了一种通过使用频繁的Galois格子来识别图形符号的新方法。我们建议在图形模式方面构建概念格但是在频繁的图形模式方面。本文的目的是双重:首先,我们尝试根据基于描述符不变的描述符到旋转,转换和缩放来识别给定图形符号的最佳原语。使用在从符号中随机选择的一组点上计算的稳定邻域计算的特征向量,将每个符号进行递减。其次,我们提出了一种基于频繁的Galois格子的新认可方法。基于频繁模式的获得的概念格式用作分类器。已经测试了该方法的检索性能和行为进行了图形识别。我们将我们的方法与其他基于不同的描述符和分类器进行了比较。我们的方法证明,用于提取频繁属性以构建频繁Galois格子的符号描述方法和算法适合于识别过程。

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