Back to articles
Volume: 16 | Article ID: art00060
Segmenting memory colours
  DOI :  10.2352/CIC.2008.16.1.art00060  Published OnlineJanuary 2008

Memory 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.

Subject Areas :
Views 6
Downloads 0
 articleview.views 6
 articleview.downloads 0
  Cite this article 

Clément Fredembach, Francisco Estrada, Sabine Süsstrunk, "Segmenting memory coloursin Proc. IS&T 16th Color and Imaging Conf.,  2008,  pp 315 - 320,

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2008
Color and Imaging Conference
color imaging conf
Society of Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151, USA