Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier
Zhao Zhang, Weiming Jiang, Jie Qin, Li Zhang, Fanzhang Li, Min Zhang, and Shuicheng Yan

TL;DR
This paper introduces a unified analysis discriminative dictionary learning framework that efficiently combines dictionary learning, sparse coding, and classification, achieving superior image recognition performance.
Contribution
It proposes a novel ADDL framework that integrates analysis dictionary learning, sparse coding with l2,1-norm, and linear classification into a single efficient model.
Findings
ADDL outperforms existing methods on real image datasets.
The model reduces training time by avoiding iterative sparse reconstruction.
It achieves higher classification accuracy with efficient computation.
Abstract
In this paper, we propose an analysis mechanism based structured Analysis Discriminative Dictionary Learning (ADDL) framework. ADDL seamlessly integrates the analysis discriminative dictionary learning, analysis representation and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learnt dictionaries, representations and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the sub-dictionaries associated with different classes to be independent. To obtain the representation coefficients, ADDL imposes a sparse l2,1-norm constraint on the coding coefficients instead of using l0 or l1-norm, since the l0 or l1-norm constraint applied in most existing DL criteria makes…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Remote-Sensing Image Classification · Sparse and Compressive Sensing Techniques
