Journal
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Volume -, Issue -, Pages 5839-5847Publisher
IEEE
DOI: 10.1109/CVPR.2016.629
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Funding
- National Nature Science Foundations of China [61273255, 61070091]
- Engineering & Technology Research Center of Guangdong Province for Big Data Analysis and Processing [2013-1589-1-11]
- Project of High Level Talents in Higher Institution of Guangdong Province [2013-2050205-47]
- Guangdong Technological Innovation Project [2013KJCX0010]
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Discriminative sparse coding has emerged as a promising technique in image analysis and recognition, which couples the process of classifier training and the process of dictionary learning for improving the discriminability of sparse codes. Many existing approaches consider only a simple single linear classifier whose discriminative power is rather weak. In this paper, we proposed a discriminative sparse coding method which jointly learns a dictionary for sparse coding and an ensemble classifier for discrimination. The ensemble classifier is composed of a set of linear predictors and constructed via both subsampling on data and subspace projection on sparse codes. The advantages of the proposed method over the existing ones are multi-fold: better discriminability of sparse codes, weaker dependence on peculiarities of training data, and more expressibility of classifier for classification. These advantages are also justified in the experiments, as our method outperformed several recent methods in several recognition tasks.
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