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Kernel descriptors  provide a unified way to generate rich visual feature sets by turning pixel attributes into patch-level features, and yield impressive results on many object recognition tasks. However, best results with kernel descriptors are achieved using efficient match kernels in conjunction with nonlinear SVMs, which makes it impractical for large-scale problems. In this paper, we propose hierarchical kernel descriptors that apply kernel descriptors recursively to form image-leveldoi:10.1109/cvpr.2011.5995719 dblp:conf/cvpr/BoLRF11 fatcat:jv4iqtjl4vhefoxeozyhlwfhgy