Hierarchical invariant sparse modeling for image analysis

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 transform 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. © 2011 IEEE.

Full Text

Duke Authors

Cited Authors

  • Bar, L; Sapiro, G

Published Date

  • 2011

Published In

Start / End Page

  • 2397 - 2400

International Standard Serial Number (ISSN)

  • 1522-4880

Digital Object Identifier (DOI)

  • 10.1109/ICIP.2011.6116125