We address a new and rapidly growing application, automated searching through large sets of images to find a pattern ``similar to this one.'' Classical matched filtering fails at this problem since patterns, particularly textures, can differ in every pixel and still be perceptually similar. Most potential recognition methods have not been tested on large sets of imagery. This paper evaluates a key recognition method on a library of almost 1000 images, based on the entire Brodatz texture album. The features used for searching rely on a significant improvement to the traditional Karhunen-Loeve (KL) transform which makes it shift-invariant. Results are shown for a variety of false alarm rates and for different subsets of KL features.
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