Feature selection with the image grand tour

David J. Marchette; Jeffrey L. Solka

doi:10.1117/12.395596

17 August 2000 Feature selection with the image grand tour

David J. Marchette, Jeffrey L. Solka

Proceedings Volume 4050, Automatic Target Recognition X; (2000) https://doi.org/10.1117/12.395596
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

The grand tour is a method for visualizing high dimensional data by presenting the user with a set of projections and the projected data. This idea was extended to multispectral images by viewing each pixel as a multidimensional value, and viewing the projections of the grand tour as an image. The user then looks for projections which provide a useful interpretation of the image, for example, separating targets from clutter. We discuss a modification of this which allows the user to select convolution kernels which provide useful discriminant ability, both in an unsupervised manner as in the image grand tour, or in a supervised manner using training data. This approach is extended to other window-based features. For example, one can define a generalization of the median filter as a linear combination of the order statistics within a window. Thus the median filter is that projection containing zeros everywhere except for the middle value, which contains a one. Using the convolution grand tour one can select projections on these order statistics to obtain new nonlinear filters.

Citation Download Citation

David J. Marchette and Jeffrey L. Solka "Feature selection with the image grand tour", Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); https://doi.org/10.1117/12.395596

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KEYWORDS

Convolution

Nonlinear filtering

Digital filtering

Image filtering

Multispectral imaging

Image processing

Feature selection

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