Getting More from Segmentation Evaluation
Getting More from Segmentation Evaluation
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从细分评估中获取更多信息
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Diana Inkpen
中科院分区：
文献类型：
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作者：
Martin Scaiano;Diana Inkpen
我们引入了一种新的分割评价方法WinPR，它解决了WindowDiff的一些局限性。WinPR区分假阳性和假阴性错误；产生更直观的度量，如精确度、召回率和F-MEASURE；对窗口大小不敏感，允许我们定制差错敏感度；基于计数错误而不是窗口，但仍为差错提供部分奖励。
We introduce a new segmentation evaluation measure, WinPR, which resolves some of the limitations of WindowDiff. WinPR distinguishes between false positive and false negative errors; produces more intuitive measures, such as precision, recall, and F-measure; is insensitive to window size, which allows us to customize near miss sensitivity; and is based on counting errors not windows, but still provides partial reward for near misses.