DocumentCode :
2955876
Title :
Positive definite dictionary learning for region covariances
Author :
Sivalingam, Ravishankar ; Boley, Daniel ; Morellas, Vassilios ; Papanikolopoulos, Nikolaos
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of Minnesota, Minneapolis, MN, USA
fYear :
2011
fDate :
6-13 Nov. 2011
Firstpage :
1013
Lastpage :
1019
Abstract :
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.
Keywords :
computer vision; covariance matrices; image representation; sparse matrices; computer vision; concise dictionary; dictionary update technique; face data; image processing; positive definite dictionary learning; positive definite matrices; positive definite sparse coding; region covariances; sparse modeling; sparse representation; Dictionaries; Encoding; Learning systems; Sparse matrices; Symmetric matrices; Training; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision (ICCV), 2011 IEEE International Conference on
Conference_Location :
Barcelona
ISSN :
1550-5499
Print_ISBN :
978-1-4577-1101-5
Type :
conf
DOI :
10.1109/ICCV.2011.6126346
Filename :
6126346
Link To Document :
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