Back to articles
Articles
Volume: 9 | Article ID: art00008
Image
Automatic Color Correction based on Generic Content Based Image Analysis
Michael Schröder
Stefan Moser
  DOI :  10.2352/CIC.2001.9.1.art00008  Published Online :  January 2001
Abstract

In this paper, we present a novel concept of color correction for consumer digital still camera (DSC) images. This concept is based on a hierarchical Bayesian image content analysis consisting of feature extraction and unsupervised clustering and on a set of color correction algorithms that have been optimized on the obtained characteristic image classes. Since the concept uses Bayesian inference to combine several color correction results, further available information (e.g., obtained from camera metadata) can be easily integrated into the color correction process.

Journal Title : Color and Imaging Conference
Publisher Name : Society of Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151, USA
Subject Areas :
Views 12
Downloads 0
 DOI 10.2352/CIC.2001.9.1.art00008
 articleview.views 12
 articleview.downloads 0
  Cite this article 

Michael Schröder, Stefan Moser, "Automatic Color Correction based on Generic Content Based Image Analysis"  in Proc. IS&T 9th Color and Imaging Conf.,  2001,  pp 41 - 45,  https://doi.org/10.2352/CIC.2001.9.1.art00008

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2001
72010350
Color and Imaging Conference
color imaging conf
2166-9635
Society of Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151, USA
2166-9635(20010101)2001:1L.41;1-
cic_v2001n1/splitsection8.xml
/ist/cic/2001/00002001/00000001/art00008
Articles
Automatic Color Correction based on Generic Content Based Image Analysis
SchröderMichael
MoserStefan
01012001
2001
1
41
45
2001
In this paper, we present a novel concept of color correction for consumer digital still camera (DSC) images. This concept is based on a hierarchical Bayesian image content analysis consisting of feature extraction and unsupervised clustering and on a set of color correction algorithms that have been optimized on the obtained characteristic image classes. Since the concept uses Bayesian inference to combine several color correction results, further available information (e.g., obtained from camera metadata) can be easily integrated into the color correction process.