Hierarchical invariant sparse modeling for image analysis

Leah Bar, Guillermo Sapiro
2011 2011 18th IEEE International Conference on Image Processing  
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 Rapid
more » ... rm and max pooling to attain rotation and scale invariance. The invariant sparse representation of patterns here presented-can be used in different object recognition tasks. Promising results are obtained for three applications -2D shapes classification, texture recognition and object detection.
doi:10.1109/icip.2011.6116125 dblp:conf/icip/BarS11 fatcat:sf7abzro5nccdk6wcwofv6i3d4