Skeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks
This work addresses a critical bottleneck for researchers in computer vision by providing standardized GT for skeleton extraction, enabling fair comparisons and reproducibility, though it is incremental as it builds on existing datasets and theories.
The paper tackles the lack of consistent skeleton ground truth (GT) in datasets, which hinders fair evaluation of skeleton extraction methods, by presenting a heuristic strategy and tool (SkeView) to generate GT for 17 datasets, achieving promising quality in terms of consistency and balance between simplicity and completeness.
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.