TY - GEN
T1 - Positive definite dictionary learning for region covariances
AU - Sivalingam, Ravishankar
AU - Boley, Daniel
AU - Morellas, Vassilios
AU - Papanikolopoulos, Nikolaos
PY - 2011/12/1
Y1 - 2011/12/1
N2 - Sparse models have proven to be extremely successful in image processing and computer vision, and most efforts have been focused on sparse representation of vectors. The success of sparse modeling and the popularity of region covariances have inspired the development of sparse coding approaches for positive definite matrices. While in earlier work [1], the dictionary was pre-determined, it is clearly advantageous to learn a concise dictionary adaptively from the data at hand. In this paper, we propose a novel approach for dictionary learning over positive definite matrices. The dictionary is learned by alternating minimization between the sparse coding and dictionary update stages, and two different atom update methods are described. The online versions of the dictionary update techniques are also outlined. Experimental results demonstrate that the proposed learning methods yield better dictionaries for positive definite sparse coding. The learned dictionaries are applied to texture and face data, leading to improved classification accuracy and strong detection performance, respectively.
AB - Sparse models have proven to be extremely successful in image processing and computer vision, and most efforts have been focused on sparse representation of vectors. The success of sparse modeling and the popularity of region covariances have inspired the development of sparse coding approaches for positive definite matrices. While in earlier work [1], the dictionary was pre-determined, it is clearly advantageous to learn a concise dictionary adaptively from the data at hand. In this paper, we propose a novel approach for dictionary learning over positive definite matrices. The dictionary is learned by alternating minimization between the sparse coding and dictionary update stages, and two different atom update methods are described. The online versions of the dictionary update techniques are also outlined. Experimental results demonstrate that the proposed learning methods yield better dictionaries for positive definite sparse coding. The learned dictionaries are applied to texture and face data, leading to improved classification accuracy and strong detection performance, respectively.
UR - http://www.scopus.com/inward/record.url?scp=84856679741&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84856679741&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2011.6126346
DO - 10.1109/ICCV.2011.6126346
M3 - Conference contribution
AN - SCOPUS:84856679741
SN - 9781457711015
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 1013
EP - 1019
BT - 2011 International Conference on Computer Vision, ICCV 2011
T2 - 2011 IEEE International Conference on Computer Vision, ICCV 2011
Y2 - 6 November 2011 through 13 November 2011
ER -