Evaluation measures for images segmentation are suggested. The methods compare the results of automatic segmentation with ground truth. The presented methods for assessing the similarity of the segments are based on three different approaches: the number of pixels in common, the similarity of the contours, and the location of centroids. The fuzzy approach consists of considering the significance of segment differences in relation to the size of the segments. The final measures for the whole images are based on recall and precision, widely used in information retrieval tasks. The approaches presented in this paper apply the fuzzy set theory instead of classical evaluation methods.

中文翻译：

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图像分割的模糊评估


提出了图像分割的评估措施。该方法将自动分割的结果与地面实况进行比较。所提出的评估片段相似性的方法基于三种不同的方法：共同像素的数量、轮廓的相似性以及质心的位置。模糊方法包括考虑与片段大小相关的片段差异的显着性。整个图像的最终度量基于召回率和精度，广泛应用于信息检索任务。本文提出的方法应用模糊集理论而不是经典的评估方法。