Name
Abstract | ||
|---|---|---|
Human action recognition has been an active topic in computer vision. Currently, most of the approaches to this problem can be categorized into two classes. One is based on local features, and the other is based on global features. Meanwhile, manifold learning has become successful in many problems in computer vision, but because of the high variability of human body, the application of manifold learning to human action recognition is limited. We propose a framework based on Elastic Manifold Embedding (EME), a new sparse manifold learning algorithm, together with local interest point features to handle human action recognition. The result of the new framework is very promising in comparison with state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.neucom.2012.06.011 | Neurocomputing |
Keywords | Field | DocType |
elastic manifold,manifold learning,local interest point feature,computer vision,new sparse manifold,new framework,active topic,elastic manifold embedding,human body,local feature,human action recognition | Manifold embedding,Pattern recognition,Action recognition,Manifold alignment,Artificial intelligence,Nonlinear dimensionality reduction,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
99, | 0925-2312 | 11 |
PageRank | References | Authors |
0.52 | 44 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoyu Deng | 1 | 31 | 3.74 |
| Xiao Liu | 2 | 332 | 12.53 |
| Mingli Song | 3 | 1646 | 98.10 |
| jun cheng | 4 | 851 | 69.84 |
| Jiajun Bu | 5 | 4106 | 211.52 |
| Chun Chen | 6 | 4727 | 246.28 |