Adaptive Online Multi-stroke Sketch Recognition Based on Hidden Markov Model

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.

• Type: Conference
• Year: 2005
• Where: ICMLC
• Authors: Zhengxing Sun, Wei Jiang, Jianyong Sun
• Keywords: ICMLC 2005, Machine Learning, Multi-stroke Sketch Recognition, Online Multi-stroke Sketch, Sketch Recognition