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Task-Driven Feature Pooling for Image Classification
Xie, Guo-Sen; Zhang, Xu-Yao; Shu Xiangbo; Yan Shuicheng; Cheng-Lin Liu
Conference NameIEEE International Conference on Computer Vision
Source PublicationICCV
Conference Date2015-12
Conference Place智利,圣地亚哥
AbstractFeature 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.

KeywordPooling Cnn
Document Type会议论文
Corresponding AuthorXie, Guo-Sen
Recommended Citation
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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