Yasnoff W A, Galbraith W, Bacus J W
Anal Quant Cytol. 1979 Jul-Aug;1(2):107-21.
Scene segmentation is an important element in pattern recognition problems. Previous efforts to evaluate and compare scene segmentation procedures have been largely subjective. Quantitative error measures would facilitate objective comparison of scene segmentation algorithms. A theoretical discussion leading to a new generalized quantitative error measure, G2, based on comparison of both pixel class proportions and spatial distributions of "true" and test segmentations, is presented. This error measure was tested on 14 manual segmentations and 40 gynecologic cytology specimens segmented with five different scene segmentation techniques. Results indicate that G2 seems to have the desirable properties of correlation with human observation, categorization of error allowing for weighting, invariance with picture size and ease of computation necessary for a useful scene segmentation error measure.
场景分割是模式识别问题中的一个重要元素。以往评估和比较场景分割程序的工作在很大程度上是主观的。定量误差度量将有助于对场景分割算法进行客观比较。本文给出了一个理论讨论，该讨论基于对“真实”分割和测试分割的像素类别比例及空间分布的比较，得出了一种新的广义定量误差度量G2。在14个手动分割以及用五种不同场景分割技术分割的40个妇科细胞学标本上对该误差度量进行了测试。结果表明，G2似乎具有与人类观察结果相关、允许加权的误差分类、与图像大小无关以及作为一种有用的场景分割误差度量所需的易于计算等理想特性。
