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Learning image similarity measures from choice data

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No logo availableRiver Valley TV
 Scheller Lichtenauer, Matthias Zolliker, Peter Lissner, Ingmar Preiss, Jens Urban, Philipp

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Learning image similarity measures from choice data
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6
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31
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CC Attribution - NoDerivatives 2.0 UK: England & Wales:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Abstract
We present a corpus of experimental data from psychometric studies on gamut mapping and demonstrate its use to develop image similarity measures. We investigate whether similarity measures based on luminance (SSIM) can be improved when features based on chroma and hue are added. Image similarity measures can be applied to automatically select a good image from a sample of transformed images.