Supervised Machine Learning for Grouping Sketch Diagram Strokes
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Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
ACM
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.
Description
@inproceedings{10.1145:2487381.2487383,
booktitle = {Eurographics Workshop on Sketch-Based Interfaces and Modeling},
editor = {Levent Burak Kara and Cindy Grimm},
title = {{Supervised Machine Learning for Grouping Sketch Diagram Strokes}},
author = {Stevens, Philip C and Blagojevic, Rachel and Plimmer, Beryl},
year = {2013},
publisher = {ACM},
ISSN = {1812-3503},
ISBN = {978-1-4503-2205-8},
DOI = {10.1145/2487381.2487383}
}