Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke





.......................

.......................


Publications [#265069] of Guillermo Sapiro

Papers Published

  1. Bar, L; Sapiro, G, Hierarchical dictionary learning for invariant classification, IEEE International Conference on Acoustics Speech and Signal Processing (2010), pp. 3578-3581, ISSN 1520-6149 [doi]
    (last updated on 2017/12/18)

    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. ©2010 IEEE.

 

dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320