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Erschienen in:
2013 | OriginalPaper | Buchkapitel
A Fast Pen Gesture Matching Method Based on Nonlinear Embedding
verfasst von : Yougen Zhang, Wei Deng, Hanchen Song, Lingda Wu
Erschienen in: Advances in Image and Graphics Technologies
Verlag: Springer Berlin Heidelberg
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The Nearest-Neighbor (NN) rule is a widely used technique for pen gesture recognition. It is quite simple and effective, furthermore, it allow for flexible extension of newly defined classes and training samples. However, the expensive cost of computation is a key issue need to be addressed. A fast template matching method for pen gesture recognition was proposed. We embed gesture description vectors nonlinearly into a low-dimensional space. The square Euclidean distance in the embedded space can be used as the lower bound of the distance in the original space, thus most dissimilar training templates could be rejected in the embedded space. Experiment results show that our method is almost over an order of magnitude faster than the naïve NN while achieving comparable recognition accuracy. Moreover, it does not rely on complex data structures or preprocessing, making it more suitable for applications that call for dynamic training data, such as user adaptive recognition.