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Selecting image retrieval parameters with a genetic algorithm

Image retrieval (IR) means taking a probe image and finding the most appropriate match in a (possibly very large) image database. Unlike keyword-indexing, our approach is to compute a feature vector (FV) for each image, and to compute the distance from the probe to each image in the database. As a starting point, we studied the system of Jacobs et al., developed at the University of Washington, which used the Haar wavelet transform to produce feature vectors from images. A genetic algorithm developed weighting parameters which yielded significantly improved image retrieval performance.