Name
Abstract | ||
|---|---|---|
Dictionary learning has emerged as a powerful tool for a range of image processing applications and a proper dictionary always plays a key issue to the final achievable performance. In this paper, a class-oriented discriminative dictionary learning (CODDL) method is presented for image classification applications. It takes a comprehensive consideration of multiple optimization objectives, emphasizing class discrimination of both dictionary atoms and representation coefficients. The atoms of the learned dictionary should be grouped into class level sub-dictionaries. Meanwhile, the sparse representation coefficients of an input sample should be concentrated on the sub-dictionary of the class it belongs to. Then, based on the learned class-oriented discriminative dictionary, the structured representation coefficients can thus be used for image classification with a simple and efficient classification scheme. The superior performance of the proposed algorithm is demonstrated through extensive experiments. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCSVT.2019.2918852 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | DocType | Volume |
Dictionaries,Machine learning,Training,Optimization,Task analysis,Linear programming,Image reconstruction | Journal | 30 |
Issue | ISSN | Citations |
7 | 1051-8215 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Ling | 1 | 15 | 1.11 |
| Zhenzhong Chen | 2 | 1244 | 101.41 |
| Feng Wu | 3 | 3635 | 295.09 |