Regularized Max Pooling for image categorization

We propose Regularized Max Pooling (RMP) for image classification. RMP classifies an image (or an image region) by extracting feature vectors atmultiple subwindows at multiple locations and scales. Unlike Spatial Pyramid Matching where the subwindows are defined purely based on geometric correspondence, RMP accounts for the deformation of discriminative parts. The amount of deformation and the discriminative ability for multiple parts are jointly learned during training. RMP outperforms the state-of-the-art performance by a wide margin on the challenging PASCAL VOC2012 dataset for human action recognition on still images.