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| Content Provider | ACM Digital Library |
|---|---|
| Author | Xu, Dong Tao, Dacheng Yang, Yi Zhuang, Yueting Maybank, Steve Pan, Yunhe |
| Abstract | Cartoons play important roles in many areas, but it requires a lot of labor to produce new cartoon clips. In this paper, we propose a gesture recognition method for cartoon character images with two applications, namely content-based cartoon image retrieval and cartoon clip synthesis. We first define Edge Features (EF) and Motion Direction Features (MDF) for cartoon character images. The features are classified into two different groups, namely intra-features and inter-features. An Unsupervised Bi-Distance Metric Learning (UBDML) algorithm is proposed to recognize the gestures of cartoon character images. Different from the previous research efforts on distance metric learning, UBDML learns the optimal distance metric from the heterogeneous distance metrics derived from intra-features and inter-features. Content-based cartoon character image retrieval and cartoon clip synthesis can be carried out based on the distance metric learned by UBDML. Experiments show that the cartoon character image retrieval has a high precision and that the cartoon clip synthesis can be carried out efficiently. |
| Starting Page | 311 |
| Ending Page | 320 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781605586083 |
| DOI | 10.1145/1631272.1631316 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-10-23 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Cartoon clip synthesis Cartoon image Image retrieval |
| Content Type | Text |
| Resource Type | Article |
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