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- W1506073902 abstract "Region importance maps from image understanding algorithms and human observer studies are ordered rankings of the pixel locations. Kemeny and Snell's distance (d/sub KS/), an existing measure from ordinal ranking theory, can thus be used as a similarity measure between images. We address three problems with d/sub KS/: 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/sub KS/ between two images, and we derive a normalized form d/sub KS/ with no bias whose range is independent of image size. For evaluating an algorithm where the reference data and algorithm output are ordered rankings of pixels, d/sub KS/ is subjectively superior to the correlation coefficient as a figure of merit." @default.
- W1506073902 created "2016-06-24" @default.
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- W1506073902 date "2002-11-11" @default.
- W1506073902 modified "2023-09-27" @default.
- W1506073902 title "Quantitative evaluation of rank-order similarity of images" @default.
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- W1506073902 doi "https://doi.org/10.1109/icip.2000.901001" @default.
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