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Proceedings Paper

Methodology for designing image similarity metrics based on human visual system models
Author(s): Thomas Frese; Charles A. Bouman; Jan P. Allebach
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Paper 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.

Paper Details

Date Published: 3 June 1997
PDF: 12 pages
Proc. SPIE 3016, Human Vision and Electronic Imaging II, (3 June 1997); doi: 10.1117/12.274545
Show Author Affiliations
Thomas Frese, Purdue Univ. (United States)
Charles A. Bouman, Purdue Univ. (United States)
Jan P. Allebach, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 3016:
Human Vision and Electronic Imaging II
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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