Duarte, Marco F.Davenport, Mark A.Wakin, Michael B.Laska, Jason N.Takhar, DharmpalKelly, Kevin F.Baraniuk, Richard G.2008-08-192008-08-192007-09-01M. F. Duarte, M. A. Davenport, M. B. Wakin, J. N. Laska, D. Takhar, K. F. Kelly and R. G. Baraniuk, "Multiscale random projections for compressive classification," <i>IEEE International Conference on Image Processing (ICIP),</i> 2007.https://hdl.handle.net/1911/21681We 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.engdata compressionimage codingimage classificationobject recognitionJournal articlehttp://dx.doi.org/10.1109/ICIP.2007.4379546