Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2019 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | Dictionary learning,Pattern recognition,Computer science,Artificial intelligence,Classifier (linguistics),Contextual image classification,Time complexity,Discriminative model,Computational complexity theory |
DocType | Volume | ISSN |
Journal | abs/1903.03058 | 1520-6149 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen Tang | 1 | 55 | 10.80 |
| Ashkan Panahi | 2 | 93 | 13.97 |
| Hamid Krim | 3 | 520 | 59.69 |
| Liyi Dai | 4 | 0 | 0.34 |