000129418 001__ 129418
000129418 005__ 20190316234425.0
000129418 037__ $$aCONF
000129418 245__ $$aSegmenting memory colours
000129418 260__ $$c2008
000129418 269__ $$a2008
000129418 336__ $$aConference Papers
000129418 500__ $$aFor an extended version of this paper, see C. Fredembach, F. Estrada and S. Süsstrunk, Memory colour segmentation and classification using class-specific eigenregions, accepted to the Journal of the Society for Information Display, 2009.
000129418 520__ $$aMemory colours refer to the colour of specific image classes that have the essential attribute of being perceived in a consistent manner by human observers. In colour correction or rendering tasks, this consistency implies that they have to be faithfully reproduced; their importance, in that respect, is greater than other regions in an image. Before these regions can be properly addressed, one must in general detect them. There are various schemes and attributes to do so, but the preferred method remains to segment the images into meaningful regions, a task for which many algorithms exist. Memory colours’ regions are not, however, similar in their attributes. Significant variations in shape, size, and texture do exist. As such, it is unclear whether a single algorithm is the most adapted for all of these classes. In this work, we concern ourselves with three memory colours: blue sky, green vegetation, and skin tones. Using a large database of real-world images, we (randomly) select and manually segment 900 images that contain one of the three memory colours. The same images are then automatically segmented with four classical algorithms. Using class-specific eigenregions, we are able to provide insights into the underlying structures of the considered classes and class-specific features that can be used to improve classification’s accuracy. Finally, we propose a distance measure that effectively results in determining how well is an algorithm is adapted to segment a given class.
000129418 6531_ $$aIVRG
000129418 6531_ $$amemory colors
000129418 6531_ $$aeigenregions
000129418 6531_ $$aimage segmentation
000129418 6531_ $$aimage classification
000129418 6531_ $$aimage segmentation measure
000129418 700__ $$aFredembach, Clément
000129418 700__ $$aEstrada, Francisco
000129418 700__ $$g125681$$aSüsstrunk, Sabine$$0241946
000129418 7112_ $$dNovember 10-15, 2008$$cPortland, USA$$aIS&T/SID 16th Color Imaging Conference
000129418 773__ $$tProc. of the IS&T/SID 16th Color Imaging Conference$$q315-320
000129418 8564_ $$zURL
000129418 8564_ $$uhttps://infoscience.epfl.ch/record/129418/files/eigen_color.pdf$$zn/a$$s1601326$$yn/a
000129418 909C0 $$xU10429$$0252320$$pIVRL
000129418 909CO $$qGLOBAL_SET$$pconf$$ooai:infoscience.tind.io:129418$$pIC
000129418 917Z8 $$x114218
000129418 937__ $$aLCAV-CONF-2008-045
000129418 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000129418 980__ $$aCONF