Multiscale random projections for compressive classification
Duarte, Marco F.
Davenport, Mark A.
Wakin, Michael B.
Laska, Jason N.
Kelly, Kevin F.
Baraniuk, Richard G.
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.
data compression; image coding; image classification; object recognition
Citable link to this pagehttps://hdl.handle.net/1911/21681
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- DSP Publications