Skeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks

Abstract

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.

Skeleton extraction of hard‐pen regular script based on stroke characterization and ambiguous zone detection

“…The Chinese character skeleton applicable in IESCC should satisfy the four conditions: no redundant pixels, no redundant branches, no deformed skeleton and the skeleton conforms to the topology of the original character. To solve this problem, some machine learning based skeleton extraction algorithms [5][6][7][8][9][10][11][12] have been proposed. Yang et al [9] introduced a heuristic strategy, built on an extended theory of diagnostic assumptions, for extracting Ground Truth (GT) of object skeletons in binary shapes and natural images.…”

“…To solve this problem, some machine learning based skeleton extraction algorithms [5][6][7][8][9][10][11][12] have been proposed. Yang et al [9] introduced a heuristic strategy, built on an extended theory of diagnostic assumptions, for extracting Ground Truth (GT) of object skeletons in binary shapes and natural images. The authors also developed a tool called SkeView to generate skeleton GT for 17 existing shape and image datasets.…”