Communications of the ACM
A Bayesian Framework for Semantic Content Characterization
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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Image similarity can be defined in a number of different semantic contexts. At the lowest common denominator, images may be classified as similar according to geometric properties, such as color and shape distributions. At the mid-level, a deeper image similarity may be defined according to semantic properties, such as scene content or description. We propose an even higher level of image similarity, in which domain knowledge is used to reason about semantic properties, and similarity is based on the results of reasoning. At this level, images with only slightly different (or similar) semantic descriptions may be classified as radically different (or similar), based upon the execution of the domain knowledge. For demonstration, we show experiments performed on a small database of 300 images of the retina, classified according to fourteen diagnoses.