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This paper describes an algorithm for searching image databases for images that match a specified pattern. The application in mind for this algorithm is a query system for a large library of digitized satellite images. The algorithm has two thresholds that allow the user to adjust independently the closeness of a match. One threshold controls an intensity match and the other controls a texture match. The thresholds are correlations that can be computed efficiently in the Fourier transform domain of an image, and are particularly efficient to compute when the Fourier coefficients are mostly zero. Thus the scheme works well with image-compression algorithms that replace small Fourier coefficients by zeros. For compressed images, the majority of the cost of processing such images is in computing the inverse transforms plus a few operations per pixel for nonlinear threshold operations. The quality of retrieval for this algorithm has not been evaluated at this writing. We show the use of this technique on a typical satellite image. The technique may be suitable for automatic identification of cloud-free images, for making crude classifications of land use, and for finding isolated features that have unique intensity and texture characteristics. We discuss how to generalize the algorithm from matching gray-scale intensity to color or multispectral images.
Stone et al. (Wed,) studied this question.