Online composite sketchy shape recognition based on bayesian networks

Authors:
Zhengxing Sun;Lisha Zhang;Bin Zhang
Affiliations:
State Key Lab for Novel Software Technology, Nanjing University, PR China;State Key Lab for Novel Software Technology, Nanjing University, PR China;State Key Lab for Novel Software Technology, Nanjing University, PR China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

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Abstract

This paper presents a novel approach for online multi-strokes composite sketchy shape recognition based on Bayesian Networks. By means of the definition of a double-level Bayesian networks, a classifier is designed to model the intrinsic temporal orders among the strokes effectively, where a sketchy shape is modeled with the relationships not only between a stroke and its neighbouring strokes, but also between a stroke and all of its subsequence.. The drawing-style tree is then adopted to capture the users' accustomed drawing styles and simplify the training and recognition of Bayesian network classifier. The experiments prove both effectiveness and efficiency of the proposed method.