Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 47, Issue 11, Pages 3758-3771Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2581861
Keywords
Discriminative dictionary learning (DDL); group sparse; image classification; low rank
Categories
Funding
- National Natural Science Foundation of China [61173090, 61072106, 61271302]
- National Research Foundation for the Doctoral Program of Higher Education of China [20130203110009]
- National Basic Research Program (973 Program) of China [2013CB329402]
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Discriminative dictionary learning (DDL) framework has been widely used in image classification which aims to learn some class-specific feature vectors as well as a representative dictionary according to a set of labeled training samples. However, interclass similarities and intraclass variances among input samples and learned features will generally weaken the representability of dictionary and the discrimination of feature vectors so as to degrade the classification performance. Therefore, how to explicitly represent them becomes an important issue. In this paper, we present a novel DDL framework with two-level low rank and group sparse decomposition model. In the first level, we learn a class-shared and several class-specific dictionaries, where a low rank and a group sparse regularization are, respectively, imposed on the corresponding feature matrices. In the second level, the class-specific feature matrix will be further decomposed into a low rank and a sparse matrix so that intraclass variances can be separated to concentrate the corresponding feature vectors. Extensive experimental results demonstrate the effectiveness of our model. Compared with the other state-of-the-arts on several popular image databases, our model can achieve a competitive or better performance in terms of the classification accuracy.
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