基于信息粒化和模糊决策的图像相似性分析

G. Vachkov
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引用次数: 3

摘要

本文提出了一种图像模糊相似度分析与分类的计算方案,该方案首先采用信息粒化处理,然后采用模糊决策处理。本文介绍了一种特殊的新版本的生长无监督学习算法用于信息粒化。它将图像的原始ldquoraw数据(RGB像素)减少到数量相当少的信息颗粒(神经元)。然后从每张图像中提取两个特征,分别是图像的重心和加权平均尺寸。这些特征进一步用作特殊模糊推理程序的输入,该程序以数值方式计算给定图像对的相似度。最后,使用预定义阈值的分类过程对所有可用图像进行分类。以18幅花卉图像为例,对所提出的相似性和分类方案进行了说明。本文还讨论了模糊推理过程参数的适当调整对于获得似是而非的近似结果是非常重要的。因此,本文还提出了一种简单的经验方法来选择这些参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Similarity analysis of images based on information granulation and fuzzy decision
This paper proposes a computational scheme for fuzzy similarity analysis and classification of images that uses first an information granulation procedure followed by a subsequent fuzzy decision procedure. A special new version of the growing unsupervised learning algorithm is introduced in the paper for information granulation. It reduces the original ldquoraw datardquo (the RGB pixels) of the image to a considerably smaller number of information granules (neurons). After that two features are extracted from each image, as follows: the center-of-gravity and the weighted average size of the image. These features are further used as inputs of a special fuzzy inference procedure that computes numerically the similarity degree for a given pair if images. Finally, a sorting procedure with a predefined threshold is used to obtain the classification results for all available images. The proposed similarity and classification scheme is illustrated on the example of 18 images of flowers. It is also discussed in the paper that the appropriate tuning of the parameters of the fuzzy inference procedure is quite important for obtaining plausible, humanlike results Therefore a simple empirical process for selection of these parameters is also suggested in the paper.
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