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Volume: 11 | Article ID: art00011
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Region-Based Image Classification for Automatic Color Correction
Clément Fredembach
Michael Schröder
Sabine Süsstrunk
  DOI :  10.2352/CIC.2003.11.1.art00011  Published Online :  January 2003
Abstract

This work presents a framework for image classification based on region information. The main application of this framework is for automatic color correction of real-scene images. Class specific color correction is already implemented in some systems, but image classification usually relies on global image features only. Our region-based approach shows a significant improvement over previously achieved classification for a variety of critical image classes. Improved image classification can directly be translated into improved automatic color correction.

Journal Title : Color and Imaging Conference
Publisher Name : Society of Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151, USA
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 DOI 10.2352/CIC.2003.11.1.art00011
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Clément Fredembach, Michael Schröder, Sabine Süsstrunk, "Region-Based Image Classification for Automatic Color Correction"  in Proc. IS&T 11th Color and Imaging Conf.,  2003,  pp 59 - 65,  https://doi.org/10.2352/CIC.2003.11.1.art00011

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Copyright © Society for Imaging Science and Technology 2003
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(20030101)2003:1L.59;1-
cic_v2003n1/splitsection11.xml
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Articles
Region-Based Image Classification for Automatic Color Correction
FredembachClément
SchröderMichael
SüsstrunkSabine
01012003
2003
1
59
65
2003
This work presents a framework for image classification based on region information. The main application of this framework is for automatic color correction of real-scene images. Class specific color correction is already implemented in some systems, but image classification usually relies on global image features only. Our region-based approach shows a significant improvement over previously achieved classification for a variety of critical image classes. Improved image classification can directly be translated into improved automatic color correction.