Retrieval-based cartoon gesture recognition and applications via semi-supervised heterogeneous classifiers learning

Publication Type:
Journal Article
Citation:
Pattern Recognition, 2013, 46 (1), pp. 412 - 423
Issue Date:
2013-01-01
Full metadata record
Files in This Item:
Filename Description Size
1-s2.0-S0031320312002956-main.pdfPublished Version2.42 MB
Adobe PDF
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. © 2012 Elsevier Ltd All rights reserved.
Please use this identifier to cite or link to this item: