Adaptive online multi-stroke sketch recognition based on hidden markov model

Authors:
Zhengxing Sun;Wei Jiang;Jianyong Sun
Affiliations:
State Key Lab for Novel Software Technology, Nanjing University, Nanjing;State Key Lab for Novel Software Technology, Nanjing University, Nanjing;State Key Lab for Novel Software Technology, Nanjing University, Nanjing
Venue:
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Year:
2005

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Abstract

This paper presents a novel approach for adaptive online multi-stroke sketch recognition based on Hidden Markov Model (HMM). The method views the drawing sketch as the result of a stochastic process that is governed by a hidden stochastic model and identified according to its probability of generating the output. To capture a user’s drawing habits, a composite feature combining both geometric and dynamic characteristics of sketching is defined for sketch representation. To implement the stochastic process of online multi-stroke sketch recognition, multi-stroke sketching is modeled as an HMM chain while the strokes are mapped as different HMM states. To fit the requirement of adaptive online sketch recognition, a variable state-number determining method for HMM is also proposed. The experiments prove both the effectiveness and efficiency of the proposed method.