Learning from neighboring strokes: Combining appearance and context for multi-domain sketch recognition
Author(s)Ouyang, Tom Yu; Davis, Randall
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We propose a new sketch recognition framework that combines a rich representation of low level visual appearance with a graphical model for capturing high level relationships between symbols. This joint model of appearance and context allows our framework to be less sensitive to noise and drawing variations, improving accuracy and robustness. The result is a recognizer that is better able to handle the wide range of drawing styles found in messy freehand sketches. We evaluate our work on two real-world domains, molecular diagrams and electrical circuit diagrams, and show that our combined approach significantly improves recognition performance.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Advances in Neural Information Processing Systems 22 (NIPS 2008)
Neural Information Processing Systems Foundation, Inc.
Ouyand, Tom Y. and Randall Davis. "Learning from Neighboring Strokes: Combing Appearance and Context for Multi-Domain Sketch Recognition." Advances in Neural Information Processing Systems 22 (NIPS 2009), December 2008, Vancouver, B.C., Canada, edited by D. Koller, Neural Information Processing Systems Foundation, Inc., 2009.
Final published version