ClassySeg: A Machine Learning Approach to AutomaticStroke Segmentation

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Date
2011
Authors
Herold, J.
Stahovich, T. F.
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
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 points, consisting of all curvature maxima. Features are computed for each candidate point based on speed, curvature, and other geometric properties. 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 points are true segment points. A beam search is used to approximate the optimal subset of features to use as input to the classifier. ClassySeg is more accurate than previous techniques foruser-independent training conditions, and is as good as the current state-of-the-art algorithm for user-optimized conditions. More importantly, ClassySeg represents a movement away from prior heuristic-based approaches towards a more general and extensible approach.
Description

        
@inproceedings{
10.2312:SBM/SBM11/109-116
,
booktitle = {
Eurographics Workshop on Sketch-Based Interfaces and Modeling
},
editor = {
Tracy Hammond and Andy Nealen
},
title = {{
ClassySeg: A Machine Learning Approach to AutomaticStroke Segmentation
}},
author = {
Herold, J.
 and 
Stahovich, T. F.
},
year = {
2011
},
publisher = {
The Eurographics Association
},
ISSN = {
1812-3503
},
ISBN = {
978-1-4503-0906-6
},
DOI = {
10.2312/SBM/SBM11/109-116
}
}
Citation
URI
http://dx.doi.org/10.2312/SBM/SBM11/109-116
Collections
SBM11: Sketch Based Interfaces and Modeling 2011