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- W2089917685 abstract "2D cartoon plays an important role in many areas, but it requires effective methods to relieve manual labors. In this paper, we propose a heterogeneous cartoon gesture recognition method with applications. Firstly, heterogeneous features with different dimensions are assigned to express cartoon and human-subject images according to their characteristics. Then for recognition, we simultaneously integrate shared structure learning (SSL) and graph-based transductive learning into a joint framework to learn reliable classifiers on heterogeneous features. Provided with the framework, the similarities between cartoon and human-subject gestures can be quantitatively evaluated in a cross-feature manner. Extensive experiments on self-defined datasets have demonstrated the effectiveness of our method. Finally, applications illustrate the usages in various aspects of 2D cartoon industry." @default.
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- W2089917685 date "2013-01-01" @default.
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- W2089917685 title "Retrieval-based cartoon gesture recognition and applications via semi-supervised heterogeneous classifiers learning" @default.
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- W2089917685 doi "https://doi.org/10.1016/j.patcog.2012.06.025" @default.
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