Name
Abstract | ||
|---|---|---|
Dictionary learning has been successfully applied in image classification. However, many dictionary learning methods that encode only a single image at a time while training, ignore correlation and other useful information contained within the entire training set. In this paper, we propose a new principle that uses the support of the coefficients to measure the similarity between the pairs of coefficients, instead of using Euclidian distance directly. More specifically, we proposed a support discrimination dictionary learning method, which finds a dictionary under which the coefficients of images from the same class have a common sparse structure while the size of the overlapped signal support of different classes is minimised. In addition, adopting a shared dictionary in a multi-task learning setting, this method can find the number and position of associated dictionary atoms for each class automatically by using structured sparsity on a group of images. The proposed model is extensively evaluated using various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46475-6_24 | COMPUTER VISION - ECCV 2016, PT II |
Keywords | Field | DocType |
Sparse Representation,Sparse Code,Dictionary Learning,Code Vector,Multitask Learning | Training set,ENCODE,Multi-task learning,Dictionary learning,Pattern recognition,Computer science,Sparse approximation,Euclidean distance,Correlation,Artificial intelligence,Contextual image classification | Conference |
Volume | ISSN | Citations |
9906 | 0302-9743 | 3 |
PageRank | References | Authors |
0.36 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Liu | 1 | 3 | 0.36 |
| Wei Chen | 2 | 26 | 6.38 |
| Qingchao Chen | 3 | 16 | 3.97 |
| Ian J. Wassell | 4 | 288 | 35.10 |