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Volume: 6 | Article ID: art00005
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Learning Image Similarity Measures from Choice Data
Matthias Scheller Lichtenauer
Peter Zolliker
Ingmar Lissner
Jens Preiss
Philipp Urban
  DOI :  10.2352/CGIV.2012.6.1.art00005  Published Online :  January 2012
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.

Journal Title : Conference on Colour in Graphics, Imaging, and Vision
Publisher Name : Society of Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151, USA
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 DOI 10.2352/CGIV.2012.6.1.art00005
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Matthias Scheller Lichtenauer, Peter Zolliker, Ingmar Lissner, Jens Preiss, Philipp Urban, "Learning Image Similarity Measures from Choice Data"  in Proc. IS&T CGIV 2012 6th European Conf. on Colour in Graphics, Imaging, and Vision,  2012,  pp 24 - 30,  https://doi.org/10.2352/CGIV.2012.6.1.art00005

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Copyright © Society for Imaging Science and Technology 2012
72010351
Conference on Colour in Graphics, Imaging, and Vision
conf colour graph imag vis
2158-6330
Society of Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151, USA
2158-6330(20120101)2012:1L.24;1-
cgiv_v2012n1/splitsection5.xml
/ist/cgiv/2012/00002012/00000001/art00005
Articles
Learning Image Similarity Measures from Choice Data
LichtenauerMatthias Scheller
ZollikerPeter
LissnerIngmar
PreissJens
UrbanPhilipp
01012012
2012
1
24
30
2012
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.