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    Multiscale random projections for compressive classification

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    Author
    Duarte, Marco F.
    Davenport, Mark A.
    Wakin, Michael B.
    Laska, Jason N.
    Takhar, Dharmpal
    Kelly, Kevin F.
    Baraniuk, Richard G.
    Type
    Journal 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
    2007-09-01
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    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892