Learning visual categories through a sparse representation classifier based cross-category knowledge transfer
文献类型：会议论文
| 作者 | Ying Lu; Liming Chen; Alexandre Saidi; Zhaoxiang Zhang ; Yunhong Wang
|
| 出版日期 | 2014-10-27 |
| 会议日期 | 27-30 October 2014 |
| 会议地点 | Paris, France |
| 关键词 | Visual Concept Recognition Transfer Learning Sparse Representation Computer Vision |
| 英文摘要 | To solve the challenging task of learning effective visual categories with limited training samples, we propose a new sparse representation classifier based transfer learning method, namely SparseTL, which propagates the cross-category knowledge from multiple source categories to the target category. Specifically, we enhance the target classification task in learning a both generative and discriminative sparse representation based classifier using pairs of source categories most positively and most negatively correlated to the target category. We further improve the discriminative ability of the classifier by choosing the most discriminative bins in the feature vector with a feature selection process. The experimental results show that the proposed method achieves competitive performance on the NUS-WIDE Scene database compared to several state of the art transfer learning algorithms while keeping a very efficient runtime. |
| 会议录 | ICIP 2014
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13307]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Ying Lu |
| 推荐引用方式 GB/T 7714 | Ying Lu,Liming Chen,Alexandre Saidi,et al. Learning visual categories through a sparse representation classifier based cross-category knowledge transfer[C]. 见:. Paris, France. 27-30 October 2014. |
入库方式： OAI收割
来源：自动化研究所
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