Name
Abstract | ||
|---|---|---|
Sparse representation of signals have become an important tool in computer vision. In many computer vision applications such as image denoising, image super-resolution and object recognition, sparse representations have produced remarkable performances. Sparse representation models often contain two stages: sparse coding and dictionary learning. In this paper, we propose a non-linear non-negative sparse representation model: NNK-KSVD. In the sparse coding stage, a non-linear update rule is proposed to obtain the sparse matrix. In the dictionary learning stage, the proposed model extends the kernel KSVD by embedding the non-negative sparse coding. The proposed non-negative kernel sparse representation model was evaluated on several public image datasets for the task of classification. Experimental results show that by exploiting the non-linear structure in images and utilizing the ‘additive’ nature of non-negative sparse coding, promising classification performance can be obtained. Moreover, the proposed sparse representation method was also evaluated in image retrieval tasks, competitive results were obtained. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2016.08.144 | Neurocomputing |
Keywords | Field | DocType |
Non-negative sparse coding,Kernel methods,Dictionary learning,Image classification | K-SVD,Computer science,Image retrieval,Artificial intelligence,Contextual image classification,Sparse matrix,Kernel (linear algebra),Computer vision,Pattern recognition,Neural coding,Sparse approximation,Kernel method,Machine learning | Journal |
Volume | ISSN | Citations |
269 | 0925-2312 | 1 |
PageRank | References | Authors |
0.35 | 28 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yungang Zhang | 1 | 87 | 10.05 |
| Tianwei Xu | 2 | 19 | 5.29 |
| Jieming Ma | 3 | 26 | 10.15 |