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A novel method for resemblance images using near fuzzy set

机译:基于近模糊集的相似度图像新方法

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We introduce a new method Near-Fuzzy set for analysis image. Indeed, near sets are considered a generalization of the rough sets theory. A set X is close to another set Y insofar as the description of at least one of the object of X corresponds to the description of least one of objects of Y. Find the tolerance classes with objects of the same description is a major problem. Maximal Clique Enumeration Algorithm solves the same problem and improves field performance image resemblance. We propose an innovative technique that hybrids both near sets approach with Fuzzy sets approaches. In this paper we use the Near-Fuzzy sets method to obtain better results in the resemblance of facial images. The performance of use of near set approach has been proved throughout the Japanese Female Facial Expression (JAFFE) database.
机译:我们介绍了一种用于分析图像的近模糊集新方法。实际上,近集被认为是粗糙集理论的推广。只要X的至少一个对象的描述与Y的至少一个对象的描述相对应,一组X就与另一组Y接近。寻找具有相同描述的对象的公差等级是一个主要问题。最大派生枚举算法解决了相同的问题,并提高了现场性能图像的相似度。我们提出了一种创新技术,将近集方法与模糊集方法混合在一起。在本文中,我们使用近模糊集方法在面部图像的相似度上获得更好的结果。在日本女性面部表情(JAFFE)数据库中,已经证明了使用近似集方法的性能。

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