Name
Abstract | ||
|---|---|---|
An efficient and flexible dictionary designing algorithm is proposed for sparse and redundant signal representation. The proposed Augmented Dictionary (AD) is based on a new dictionary model with an augmented form compared to the conventional model. With this model, we can bridge the gap between the classic dictionary learning approaches, which have general structure yet lack computational efficiency, and the artificial neural network theory, which has potential high parallel computational efficiency but poor universality of structure. In this paper, we discuss the advantages of augmented dictionary, and interpret how the augmented dictionary can be trained with labeled samples. The proposed neural network based augmented dictionary designing method enjoys some important features, such as high accuracy, strong robustness and desired computational efficiency. As a demonstration of these benefits, we present high-quality hyperspectral image classification results based on the new algorithm. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | dictionary, learning, neural network, hyperspectral, classification |
Field | DocType | ISSN |
Matching pursuit,Computer vision,Algorithm design,Dictionary learning,K-SVD,Pattern recognition,Computer science,Sparse approximation,Robustness (computer science),Hyperspectral imaging,Artificial intelligence,Artificial neural network | Conference | 2153-6996 |
Citations | PageRank | References |
1 | 0.35 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Qv | 1 | 4 | 2.42 |
| Jihao Yin | 2 | 90 | 12.18 |
| Charles A DiMarzio | 3 | 66 | 13.84 |