Philip C. Stevens
Beryl Plimmer
ABSTRACT
Grouping of strokes into semantically meaningful diagram elements is a difficult problem. Yet such grouping is needed if truly natural sketching is to be supported in intelligent sketch tools. Using a machine learning approach, we propose a number of new paired-stroke features for grouping and evaluate the suitability of a range of algorithms. Our evaluation shows the new features and algorithms produce promising results that are statistically better than the existing machine learning grouper.
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Index Terms
- Supervised machine learning for grouping sketch diagram strokes
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