Supervised machine learning for grouping sketch diagram strokes

Authors:
Philip C. Stevens;Rachel Blagojevic;Beryl Plimmer
Affiliations:
University of Auckland, Auckland, New Zealand;University of Auckland, Auckland, New Zealand;University of Auckland, Auckland, New Zealand
Venue:
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
Year:
2013

Citing 19
Cited 0

Specifying gestures by example

Proceedings of the 18th annual conference on Computer graphics and interactive techniques
A perceptually-supported sketch editor

UIST '94 Proceedings of the 7th annual ACM symposium on User interface software and technology
Designing PETS: a personal electronic teller of stories

Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Random Forests

Machine Learning
Multiclass Alternating Decision Trees

ECML '02 Proceedings of the 13th European Conference on Machine Learning
Discerning Structure from Freeform Handwritten Notes

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Perceptually-supported image editing of text and graphics

Proceedings of the 16th annual ACM symposium on User interface software and technology
Hierarchical parsing and recognition of hand-sketched diagrams

Proceedings of the 17th annual ACM symposium on User interface software and technology
Dynamically constructed Bayes nets for multi-domain sketch understanding

ACM SIGGRAPH 2007 courses
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes

Proceedings of the 20th annual ACM symposium on User interface software and technology
Properties of Real-World Digital Logic Diagrams

PLT '07 Proceedings of the First International Workshop on Pen-Based Learning Technologies
PaleoSketch: accurate primitive sketch recognition and beautification

Proceedings of the 13th international conference on Intelligent user interfaces
Combining geometry and domain knowledge to interpret hand-drawn diagrams

Computers and Graphics
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
SketchNode: intelligent sketching support and formal diagramming

Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction
Recognizing sketched multistroke primitives

ACM Transactions on Interactive Intelligent Systems (TiiS)
Technical Section: Using data mining for digital ink recognition: Dividing text and shapes in sketched diagrams

Computers and Graphics
Rata.gesture: A gesture recognizer developed using data mining

Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Spatial recognition and grouping of text and graphics

SBM'04 Proceedings of the First Eurographics conference on Sketch-Based Interfaces and Modeling

Quantified Score

Hi-index	0.00

Visualization

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.