| Paper: | TP-L2.4 |
| Session: | Image and Video Filtering and Multiresolution Processing |
| Time: | Tuesday, September 18, 15:30 - 15:50 |
| Presentation: |
Lecture
|
| Title: |
TENSOR-BASED FILTER DESIGN USING KERNEL RIDGE REGRESSION |
| Authors: |
Christian Bauckhage; Deutsche Telekom Laboratories | | |
| Abstract: |
Tensor-based approaches to visual object detection can drastically reduce the number of parameters in the training process. Compared to their vector-based counterparts, tensor methods therefore train faster, better manage noisy or corrupted training samples, and are less prone to over-fitting. In this paper, we show how to incorporate the kernel trick into tensor-based filter design. Dealing with object detection in cluttered natural environments, the method is shown to cope with substantially varying training data and a cascade of only two kernel tensor-filters is demonstrated to provide very reliable results. |
