@inproceedings{cd42818c7bbe4346ace3c9f85b4bb034,
title = "Hierarchical dictionary learning for invariant classification",
abstract = "Sparse representation theory has been increasingly used in the fields of signal processing and machine learning. The standard sparse models are not invariant to spatial transformations such as image rotations, and the representation is very sensitive even under small such distortions. Most studies addressing this problem proposed algorithms which either use transformed data as part of the training set, or are invariant or robust only under minor transformations. In this paper we suggest a framework which extracts sparse features invariant under significant rotations and scalings. The algorithm is based on a hierarchical architecture of dictionary learning for sparse coding in a cortical (log-polar) space. The proposed model is tested in supervised classification applications and proved to be robust under transformed data.",
keywords = "Classification, Dictionary learning, Hierarchy, Invariance, Log-polar, Sparse models",
author = "Leah Bar and Guillermo Sapiro",
year = "2010",
doi = "10.1109/ICASSP.2010.5495916",
language = "English (US)",
isbn = "9781424442966",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3578--3581",
booktitle = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings",
address = "United States",
note = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 ; Conference date: 14-03-2010 Through 19-03-2010",
}