Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with the popularity of deep learning techniques. Furthermore, we see skeleton GTs used not only for training skeleton detectors with Convolutional Neural Networks (CNN) but also for evaluating skeleton-related pruning and matching algorithms. However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards. As a result, it is difficult to evaluate and reproduce CNN-based skeleton detectors and algorithms on a fair basis. In this paper, we present a heuristic strategy for object skeleton GT extraction in binary shapes and natural images. Our strategy is built on an extended theory of diagnosticity hypothesis, which enables encoding human-in-the-loop GT extraction based on clues from the target's context, simplicity, and completeness. Using this strategy, we developed a tool, SkeView, to generate skeleton GT of 17 existing shape and image datasets. The GTs are then structurally evaluated with representative methods to build viable baselines for fair comparisons. Experiments demonstrate that GTs generated by our strategy yield promising quality with respect to standard consistency, and also provide a balance between simplicity and completeness.

翻译：骨架真值对于监督式骨架提取方法的成功至关重要，尤其在深度学习技术日益普及的背景下。此外，骨架真值不仅用于训练基于卷积神经网络的骨架检测器，还用于评估与骨架相关的剪枝和匹配算法。然而，现有大多数形状和图像数据集存在骨架真值缺失及真值标准不一致的问题，导致基于卷积神经网络的骨架检测器与算法难以在公平基础上进行复现与评估。本文针对二值形状和自然图像，提出一种启发式目标骨架真值提取策略。该策略基于扩展的诊断性假设理论，通过编码基于目标上下文线索、简洁性和完整性的"人在回路"式真值提取方法。基于此策略，我们开发了工具SkeView，为17个现有形状和图像数据集生成骨架真值。随后采用代表性方法对生成的骨架真值进行结构性评估，构建可行的公平比较基线。实验表明，本策略生成的真值在标准一致性方面具有优良质量，并在简洁性与完整性之间取得平衡。