A machine learning approach to automatic stroke segmentation

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• Machine learning based segmentation.
• Combines strengths of prior approaches.
• More accurate than the state-of-the art.
• Highly extensible.
• Segmentation is useful for semantic labeling.

We present ClassySeg, a technique for segmenting hand-drawn pen strokes into lines and arcs. ClassySeg employs machine learning techniques to infer the segmentation intended by the drawer. The technique begins by identifying a set of candidate segment windows, each comprising a curvature maximum and its neighboring points. Features are computed for each point in each window based on curvature and other geometric properties. Most of these features are adapted from numerous prior segmentation approaches, effectively combining their strengths. These features are used to train a statistical classifier to identify which candidate windows contain true segment points. ClassySeg is more accurate than previous techniques for both user-independent and user-optimized training conditions. More importantly, ClassySeg represents a movement away from prior, heuristic-based approaches, toward a more general and extensible technique.

ClassySeg is a technique for identifying the intended segmentation points, indicated in the figure as large points, of hand-drawn pen strokes.Figure optionsDownload high-quality image (46 K)Download as PowerPoint slide