Multiscale Random Projections for Compressive Classification

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.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Marco F. Duarte, Mark A. Davenport, Michael B. Wakin, Jason N. Laska, Dharmpal Takhar, Kevin F. Kelly, Richard G. Baraniuk
• Keywords: Compressive Image Projections, Dimension-reducing Random Projections, ICIP 2007, Image Processing, Random Projections