Ordinal information and preference structures: decision models and applications
Ordinal information and preference structures: decision models and applications
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
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There are needs for evaluating rank order-based similarity between images. Region importance maps from image understanding algorithms or human observer studies are ordered rankings of the pixel locations. We address three problems with Kemeny and Snell's distance (d_{KS}), an existing measure from ordinal ranking theory, when applied to images: its high-computational cost, its bias in favor of images with sparse histograms, and its image-size dependent range of values. We present a novel computationally efficient algorithm for computing d_{KS} between two images and we derive a normalized form \hat{d}_{KS} with no bias whose range is independent of image size. For evaluating similarity between images that can be considered as ordered rankings of pixels, \hat{d}_{KS} is subjectively superior to cross correlation.