Methodology for designing image similarity metrics based on human visual system models

Thomas Frese; Charles A. Bouman; Jan P. Allebach

doi:10.1117/12.274545

3 June 1997 Methodology for designing image similarity metrics based on human visual system models

Thomas Frese, Charles A. Bouman, Jan P. Allebach

Proceedings Volume 3016, Human Vision and Electronic Imaging II; (1997) https://doi.org/10.1117/12.274545
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

Abstract

In this paper 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 form 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 computed by the metric to the choices made by the human subject. The results indicate that features corresponding to contrast, color-contrast and orientation can significantly improve search performance. Furthermore, the systematic optimization and evaluation strategy using the visual test is a general tool for designing and evaluating image similarity metrics.

Citation Download Citation

Thomas Frese, Charles A. Bouman, and Jan P. Allebach "Methodology for designing image similarity metrics based on human visual system models", Proc. SPIE 3016, Human Vision and Electronic Imaging II, (3 June 1997); https://doi.org/10.1117/12.274545

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