Task-Driven Feature Pooling for Image Classification
文献类型：会议论文
| 作者 | Xie, Guo-Sen ; Zhang, Xu-Yao; Shu Xiangbo; Yan Shuicheng; Cheng-Lin Liu
|
| 出版日期 | 2015 |
| 会议日期 | 2015-12 |
| 会议地点 | 智利，圣地亚哥 |
| 关键词 | Pooling Cnn |
| 英文摘要 | Feature pooling is an important strategy to achieve high performance in image classification. However, most pooling methods are unsupervised and heuristic. In this paper, we propose a novel task-driven pooling (TDP) model to directly learn the pooled representation from data in a discriminative manner. Different from the traditional methods (e.g., average and max pooling), TDP is an implicit pooling method which elegantly integrates the learning of representations into the given classification task. The optimization of TDP can equalize the similarities between the descriptors and the learned representation, and maximize the classification accuracy. TDP can be combined with the traditional BoW models (coding vectors) or the recent stateof-the-art CNN models (feature maps) to achieve a much better pooled representation. Furthermore, a self-training mechanism is used to generate the TDP representation for a new test image. A multi-task extension of TDP is also proposed to further improve the performance. Experiments on three databases (Flower-17, Indoor-67 and Caltech-101) well validate the effectiveness of our models. |
| 会议录 | ICCV
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/11956]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 通讯作者 | Xie, Guo-Sen |
| 推荐引用方式 GB/T 7714 | Xie, Guo-Sen,Zhang, Xu-Yao,Shu Xiangbo,et al. Task-Driven Feature Pooling for Image Classification[C]. 见:. 智利，圣地亚哥. 2015-12. |
入库方式： OAI收割
来源：自动化研究所
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