Skeleton Ground Truth Extraction: Methodology, Annotation Tool and
  Benchmarks
Cong Yang Bipin Indurkhya John See Bo Gao Yan Ke Zeyd Boukhers Zhenyu Yang Marcin Grzegorzek
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.