The problem of measuring perceptual similarity between images is addressed using a new image model based on the Wold decomposition. The model permits separate treatment of image components which correspond approximately to periodicity, directionality, and randomness. We compare its performance in an image search application to two other methods -- one based on shift-invariant principle components and one based on a multiscale simultaneous auto-regressive model. When textured images are ordered by distances between their Wold components, the results appear to be much closer to the human perception of similarity. We discuss how decoupling the three components can increase flexibility for measuring image similarity and can save computation, permitting the ``quickest'' matches when the features are the most perceptually ``salient.''
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