Name
Abstract | ||
|---|---|---|
In this paper, we tackle the challenge of action recognition by building robust models from Extreme Learning Machines (ELM). Applying this approach from reduced preprocessed feature vectors on the Microsoft Research Cambridge-12 (MSRC-12) Kinect gesture dataset outperforms the state-of-the-art results with an average correct classification rate of 0.953 over 20 runs, when splitting in two equal subsets for training and testing the 6, 244 action instances. This ELM based proposal using a multi-quadric radial basis activation function is compared to other classical classification strategies such as Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) and advancements are also presented in terms of execution times. |
Year | Venue | Field |
|---|---|---|
2016 | VISIGRAPP (4: VISAPP) | Feature vector,Pattern recognition,Gesture,Computer science,Activation function,Action recognition,Support vector machine,Artificial intelligence,Classification rate,Perceptron,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
14 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gregoire Lefebvre | 1 | 82 | 12.13 |
| Julien Cumin | 2 | 3 | 1.41 |