度量(数据仓库)
计算机科学
帕斯卡(单位)
二进制数
分割
人工智能
突出
一般化
模式识别(心理学)
财产(哲学)
对象(语法)
计算机视觉
数据挖掘
数学
哲学
程序设计语言
数学分析
认识论
算术
作者
Ran Margolin,Lihi Zelnik‐Manor,Ayellet Tal
摘要
The output of many algorithms in computer-vision is either non-binary maps or binary maps (e.g., salient object detection and object segmentation). Several measures have been suggested to evaluate the accuracy of these foreground maps. In this paper, we show that the most commonly-used measures for evaluating both non-binary maps and binary maps do not always provide a reliable evaluation. This includes the Area-Under-the-Curve measure, the Average-Precision measure, the F-measure, and the evaluation measure of the PASCAL VOC segmentation challenge. We start by identifying three causes of inaccurate evaluation. We then propose a new measure that amends these flaws. An appealing property of our measure is being an intuitive generalization of the F-measure. Finally we propose four meta-measures to compare the adequacy of evaluation measures. We show via experiments that our novel measure is preferable.
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