@inproceedings{af38b83035ab445ea9fc0ebf86d62302,
title = "Hierarchical invariant sparse modeling for image analysis",
abstract = "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.",
keywords = "dictionary learning, Feature extraction, hierarchical models, invariant representation, sparse coding",
author = "Leah Bar and Guillermo Sapiro",
year = "2011",
doi = "10.1109/ICIP.2011.6116125",
language = "English (US)",
isbn = "9781457713033",
series = "Proceedings - International Conference on Image Processing, ICIP",
pages = "2397--2400",
booktitle = "ICIP 2011",
note = "2011 18th IEEE International Conference on Image Processing, ICIP 2011 ; Conference date: 11-09-2011 Through 14-09-2011",
}