Multiscale random projections for compressive classification

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Title: Multiscale random projections for compressive classification
Author: Duarte, Marco F.; Davenport, Mark A.; Wakin, Michael B.; Laska, Jason N.; Takhar, Dharmpal; Kelly, Kevin F.; Baraniuk, Richard G.
Type: Article
Citation: M. 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," IEEE International Conference on Image Processing (ICIP), 2007.
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.
Date Published: 2007-09-01

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  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.