Analysis Dictionary Learning: An Efficient and Discriminative Solution
This is an incremental improvement for image classification researchers seeking faster discriminative dictionary learning methods.
The authors tackled the computational complexity of discriminative dictionary learning for image classification by proposing an efficient convolutional analysis dictionary learning method, which reduces time complexity in training and testing while achieving competitive accuracy on standard databases.
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.