The human visual system proves expert in discovering patterns in both global and local feature space. Can we design a similar way for unsupervised feature learning? In this paper, we propose a novel spatial pooling method within an unsupervised feature learning framework, named  Rich and Robust Feature Pooling    (  R 2  FP         R    2    FP        ), to better extract rich and robust representation from sparse feature maps learned from the raw data. Both local and global pooling strategies are further considered to instantiate such a method. The former selects the most representative features in the sub-region and summarizes the joint distribution of the selected features, while the latter is utilized to extract multiple resolutions of features and fuse the features with a feature balance kernel for rich representation. Extensive experiments on several image recognition tasks demonstrate the superiority of the proposed method.
['Wei Xiong', 'Lefei Zhang', 'Bo Du', 'Dacheng Tao']
Combining local and global: Rich and robust feature pooling for visual recognition