Name
Abstract | ||
|---|---|---|
Sparse representation of signals have become an important tool in computer vision. In many applications in computer vision, such as image denoising, image super-resolution and object recognition, sparse representations have produced remarkable performance. In this paper, we propose a non-linear non-negative sparse coding model NNK-KSVD. The proposed model extended the kernel KSVD by embedding the nonnegative sparse coding. 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23989-7_54 | INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING: IMAGE AND VIDEO DATA ENGINEERING, ISCIDE 2015, PT I |
Keywords | Field | DocType |
Non-negative sparse coding, Kernel methods, Dictionary learning, Image classification | Kernel (linear algebra),Embedding,Radial basis function kernel,Pattern recognition,Computer science,Neural coding,Sparse approximation,Artificial intelligence,Contextual image classification,Kernel method,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | Citations |
9242 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yungang Zhang | 1 | 87 | 10.05 |
| Tianwei Xu | 2 | 1 | 0.35 |
| Jieming Ma | 3 | 26 | 10.15 |