In this report we present an image similarity metric for content-based image database search. The similarity metric is based on a multiscale model of the human visual system. This multiscale model includes channels which account for perceptual phenomena such as color, contrast, color-contrast and orientation selectivity. From these channels, we extract features and then forin an aggregate measure of similarity using a weighted linear combination of the feature differences. The choice of features and weights is :made to maximize the consistency with similarity ratings made by human subjects. In particular, we use a visual test to collect experimental image matching data. We then define a cost function relating the distances computeld by the metric to the choices made by the human subject. The results indicate that features corresponding to contrast, color-contrast and orientation selectivity can significantly improve search performance. Furthermore, the systematic optimization and evaluation strategy using the visual test is a gent:ral tool for designing and evaluating image similarity metrics.
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