Task-Driven Feature Pooling for Image Classification
Xie, Guo-Sen; Zhang, Xu-Yao; Shu Xiangbo; Yan Shuicheng; Cheng-Lin Liu
2015
会议名称IEEE International Conference on Computer Vision
会议录名称ICCV
会议日期2015-12
会议地点智利,圣地亚哥
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.

关键词Pooling Cnn
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11956
专题模式识别国家重点实验室_模式分析与学习
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],2015.
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