Name
Title | ||
|---|---|---|
Linear spatial pyramid matching using non-convex and non-negative sparse coding for image classification |
Abstract | ||
|---|---|---|
Recently sparse coding have been highly successful in image classification mainly due to its capability of incorporating the sparsity of image representation. In this paper, we propose an improved sparse coding model based on linear spatial pyramid matching(SPM) and Scale Invariant Feature Transform (SIFT) descriptors. The novelty is the simultaneous non-convex and non-negative characters added to the sparse coding model. Our numerical experiments show that the improved approach using non-convex and non-negative sparse coding is superior than the original ScSPM[1] on several typical databases. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ChinaSIP.2015.7230388 | 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) |
Keywords | Field | DocType |
Image classification,Non-convex and non-negative sparse coding,SPM,Iterative support detection | Scale-invariant feature transform,Pattern recognition,Neural coding,Sparse approximation,Regular polygon,Feature extraction,Artificial intelligence,Pyramid,Contextual image classification,Mathematics,Encoding (memory) | Journal |
Volume | Citations | PageRank |
abs/1504.06897 | 1 | 0.35 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengqiang Bao | 1 | 1 | 0.35 |
| Liangtian He | 2 | 1 | 0.68 |
| Yilun Wang | 3 | 826 | 32.74 |