Automatic correction of annotation boundaries in activity datasets by class separation maximization
Automatic correction of annotation boundaries in activity datasets by class separation maximization
复制标题
通过类分离最大化自动修正活动数据集中的注释边界
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
T. Plötz
中科院分区：
文献类型：
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作者：
Reuben Kirkham;Aftab Khan;S. Bhattacharya;Nils Y. Hammerla;Sebastian Mellor;D. Roggen;T. Plötz
为了对训练活动识别系统所需的活动数据集进行注释，精确地识别活动的边界是一项挑战。对于专家和非专家来说都是如此，他们可能会被招募到众包范例中，以减少注释工作或通过将任务分配给多个注释者来加快过程。我们提出了一种自动调整标注边界的方法，假设标注标签正确，但边界不精确，否则称为“标签抖动”。该方法最大限度地提高了福永类可分性，应用于时间序列。对标准基准数据集的评估显示，与最初的抖动注释相比，统计上有显著的改进。
t is challenging to precisely identify the boundary of activities in order to annotate the activity datasets required to train activity recognition systems. This is the case for experts, as well as non-experts who may be recruited for crowd-sourcing paradigms to reduce the annotation effort or speed up the process by distributing the task over multiple annotators. We present a method to automatically adjust annotation boundaries, presuming a correct annotation label, but imprecise boundaries, otherwise known as "label jitter". The approach maximizes the Fukunaga Class-Separability, applied to time series. Evaluations on a standard benchmark dataset showed statistically significant improvements from the initial jittery annotations.