Abstract
This paper presents a method of online sketchy shape recognition that can adapt to different user sketching styles. The adaptation principle is based on incremental active learning and dynamic user modeling. Incremental active learning is used for sketchy stroke classification such that important data can actively be selected to train the classifiers. Dynamic user modeling is used to model the user’s sketching style in an incremental decision tree, which is then used to recognize the composite shapes dynamically by means of fuzzy matching. Experiments prove the proposed method both effective and efficient for user adaptation in online sketchy shape recognition.
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Sun, Z., Liu, W., Peng, B., Zhang, B., Sun, J. (2004). User Adaptation for Online Sketchy Shape Recognition. In: Lladós, J., Kwon, YB. (eds) Graphics Recognition. Recent Advances and Perspectives. GREC 2003. Lecture Notes in Computer Science, vol 3088. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25977-0_28
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DOI: https://doi.org/10.1007/978-3-540-25977-0_28
Publisher Name: Springer, Berlin, Heidelberg
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