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Supervised Evaluation of Image Segmentation and Object Proposal Techniques

机译：图像分割和对象提议技术的监督评估

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摘要

This paper tackles the supervised evaluation of image segmentation and object proposal algorithms. It surveys, structures, and deduplicates the measures used to compare both segmentation results and object proposals with a ground truth database; and proposes a new measure: the precision-recall for objects and parts. To compare the quality of these measures, eight state-of-the-art object proposal techniques are analyzed and two quantitative meta-measures involving nine state of the art segmentation methods are presented. The meta-measures consist in assuming some plausible hypotheses about the results and assessing how well each measure reflects these hypotheses. As a conclusion of the performed experiments, this paper proposes the tandem of precision-recall curves for boundaries and for objects-and-parts as the tool of choice for the supervised evaluation of image segmentation. We make the datasets and code of all the measures publicly available.
机译：本文解决了图像分割和对象提议算法的监督评估。它将调查，构造和重复数据删除的方法与基础事实数据库进行比较，以比较细分结果和对象建议；并提出了一项新措施：对象和零件的精确召回。为了比较这些措施的质量，分析了八种最新的对象建议技术，并提出了涉及九种最新细分方法的两种定量元措施。元度量包括假设一些关于结果的合理假设，并评估每种度量对这些假设的反映程度。作为进行的实验的结论，本文提出了边界和对象与零件的精确召回曲线的串联方式，将其作为图像分割监督评估的选择工具。我们公开提供所有度量的数据集和代码。

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