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Authors: Xi Wang 1 ; Ronny Hänsch 2 ; Lizhuang Ma 1 and Olaf Hellwich 2

Affiliations: 1 Shanghai Jiao Tong University, China ; 2 Technical University of Berlin, Germany

ISBN: 978-989-758-003-1

Keyword(s): Graph-cut, Color Space, Image Segmentation.

Related Ontology Subjects/Areas/Topics: Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Segmentation and Grouping

Abstract: Graph-cut optimization has been successfully applied in many image segmentation tasks. Within this framework color information has been extensively used as a perceptual property of objects to segment the foreground object from background. There are different representations of color in digital images, each with special characteristics. Previous work on segmentation lacks a systematic study of which color space is better suited for image segmentation. This work applies the Graph Cut algorithm for image segmentation based on five different, widespread color spaces and evaluates their performance on public benchmark datasets. Most of the tested color spaces lead to similar results. Segmentations based on L*a*b* color space are of slightly higher or similar quality as all the other methods. In contrast, RGB-based segmentations are mostly worse than a segmentation based on any other tested color space.

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Paper citation in several formats:
Wang, X.; Hänsch, R.; Ma, L. and Hellwich, O. (2014). Comparison of Different Color Spaces for Image Segmentation using Graph-cut.In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 301-308. DOI: 10.5220/0004681603010308

@conference{visapp14,
author={Xi Wang. and Ronny Hänsch. and Lizhuang Ma. and Olaf Hellwich.},
title={Comparison of Different Color Spaces for Image Segmentation using Graph-cut},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={301-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004681603010308},
isbn={978-989-758-003-1},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Comparison of Different Color Spaces for Image Segmentation using Graph-cut
SN - 978-989-758-003-1
AU - Wang, X.
AU - Hänsch, R.
AU - Ma, L.
AU - Hellwich, O.
PY - 2014
SP - 301
EP - 308
DO - 10.5220/0004681603010308

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