| Paper: | WP-L6.1 |
| Session: | Object Recognition II |
| Time: | Wednesday, September 19, 14:30 - 14:50 |
| Presentation: |
Lecture
|
| Title: |
MULTISCALE RANDOM PROJECTIONS FOR COMPRESSIVE CLASSIFICATION |
| Authors: |
Marco Duarte; Rice University | | |
| | Mark Davenport; Rice University | | |
| | Michael Wakin; California Institute of Technology | | |
| | Jason Laska; Rice University | | |
| | Dharmpal Takhar; Rice University | | |
| | Kevin Kelly; Rice University | | |
| | Richard Baraniuk; Rice University | | |
| Abstract: |
We propose a framework for exploiting dimension-reducing random projections in detection and classification problems. Our approach is based on the generalized likelihood ratio test; in the case of image classification, it exploits the fact that a set of images of a fixed scene under varying articulation parameters forms a low-dimensional, nonlinear manifold. Exploiting recent results showing that random projections stably embed a smooth manifold in a lower-dimensional space, we develop the multiscale smashed filter as a compressive analog of the familiar matched filter classifier. In a practical target classification problem using a single-pixel camera that directly acquires compressive image projections, we achieve high classification rates using many fewer measurements than the dimensionality of the images. |
