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Sparse representation theory has been increasingly used in signal processing and machine learning. In this paper we introduce a hierarchical sparse modeling approach which integrates information from the image patch level to derive a mid-level invariant image and pattern representation. The proposed framework is based on a hierarchical architecture of dictionary learning for sparse coding in a cortical (log-polar) space, combined with a novel pooling operator which incorporates the Rapiddoi:10.1109/icip.2011.6116125 dblp:conf/icip/BarS11 fatcat:sf7abzro5nccdk6wcwofv6i3d4