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We present the results of a project that seeks to transform low-level features to a higher level of meaning. This project concerns a technique, latent semantic analysis (LSA), which has been used for full-text retrieval for many years. In this environment, LSA determines clusters of co-occurring keywords, sometimes, called concepts, so that a query which uses a particular keyword can then retrieve documents perhaps not containing this keyword, but containing other keywords from the same cluster. We examine the use of this technique for content-based image retrieval, using two different approaches to image feature representation.