Name
Abstract | ||
|---|---|---|
This paper investigates spatiotemporal feature extraction from temporal image sequences based on invariance representation. Invariance representation is one of important functions of the visual cortex. We propose a novel hierarchical model based on invariance and independent component analysis for spatiotemporal feature extraction. Training the model from patches sampled from natural scenes, we can obtain image basis with properties of translational, scaling, and rotational features. Further experiments on TV videos and facial image sequences show different characteristics of spatiotemporal features are achieved by training the proposed model. All these computer simulations verify that our proposed model is successful for spatiotemporal feature extraction. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4633910 | IJCNN |
Keywords | Field | DocType |
temporal image sequence,independent component analysis,invariance representation,natural scene,feature extraction,spatiotemporal feature extraction,image sequences,natural scenes,computer simulation,training data,hierarchical model,layout,visual system,correlation,computational modeling | Computer science,Artificial intelligence,Scaling,Hierarchical database model,Training set,Computer vision,Visual cortex,Pattern recognition,Invariant (physics),Feature extraction,Correlation,Independent component analysis,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7576 E-ISBN : 978-1-4244-1821-3 | 978-1-4244-1821-3 | 1 |
PageRank | References | Authors |
0.35 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenlu Yang | 1 | 28 | 7.81 |
| Liqing Zhang | 2 | 2713 | 181.40 |