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Color image retrieval using intuitionistic fuzzy sets

机译:使用直觉模糊集进行彩色图像检索

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In this paper, a new attempt is being made using Attanassov's intuitionistic fuzzy set theory for image retrieval. Intuitionistic fuzzy sets consider not only membership degree of belonging but also take into account the uncertainty involved in membership degree known as hesitation measure. Color features (expressed in various color representation systems), were intensively used (independently or jointly) during the last decade. We propose to revisit the use of color image contents as image descriptors through the introduction of fuzziness, which naturally arise from the imprecision or vagueness of the pixel color values and human perception. This has been applied in the HSV color space. Hue and value are two color features that are used to construct intuitionistic fuzzy sets; we construct two-dimensional sets which are more suitable than one-dimensional ones. Another key aspect of our method is using fuzzy quantities as a similarity measure between two intuitionistic fuzzy sets instead of a real number due to the imprecision of the similarities. To show the robustness of the proposed method, many experiments with large databases are performed and the results show the high performance of finding similar images.
机译:在本文中,正在使用Attanassov的直觉模糊集理论进行图像检索的新尝试。直觉模糊集不仅考虑隶属度,还考虑了隶属于度的不确定性,即犹豫测度。在过去的十年中,色彩特征(在各种色彩表示系统中表达)被大量使用(独立或联合使用)。我们建议通过引入模糊性来重新考虑将彩色图像内容用作图像描述符,模糊性自然是由于像素颜色值的不精确性或模糊性以及人的感知而引起的。这已在HSV颜色空间中应用。色相和值是用于构造直觉模糊集的两个颜色特征。我们构造比一维集合更合适的二维集合。我们的方法的另一个关键方面是使用模糊量作为两个直觉模糊集之间的相似性度量,而不是由于相似性的不精确性而导致的实数。为了显示所提出方法的鲁棒性,对大型数据库进行了许多实验,结果显示了找到相似图像的高性能。

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