Analysis Dictionary Learning: an Efficient and Discriminative Solution
Paper in proceeding, 2019

Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems. To further sharpen their discriminative capabilities, most state-of-the-art DL methods have additional constraints included in the learning stages. These various constraints, however, lead to additional computational complexity. We hence propose an efficient Discriminative Convolutional Analysis Dictionary Learning (DCADL) method, as a lower cost Discriminative DL framework, to both characterize the image structures and refine the interclass structure representations. The proposed DCADL jointly learns a convolutional analysis dictionary and a universal classifier, while greatly reducing the time complexity in both training and testing phases, and achieving a competitive accuracy, thus demonstrating great performance in many experiments with standard databases.


Wen Tang

North Carolina State University

Ashkan Panahi

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Hamid Krim

North Carolina State University

Liyi Dai

North Carolina State University

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

International Conference on Acoustic Speech and Signal Processing
Brighton, ,

Subject Categories


Probability Theory and Statistics

Signal Processing

Computer Science



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