Graph Cut and Image Segmentation using Mean Cut by Means of an Agglomerative Algorithm

Elaine Ayumi Chiba, Marco Antonio Garcia Carvalho, André Luís Costa

2014

Abstract

Graph partitioning, or graph cut, has been studied by several authors as a tool for image segmentation. It refers to partitioning a graph into several subgraphs such that each of them represents a meaningful object of interest in the image. In this work we propose a hierarchical agglomerative clustering algorithm driven by the cut and mean cut criteria. Some preliminary experiments were performed using the benchmark of Berkeley BSDS500 with promising results.

Paper Citation

in Harvard Style

Chiba E., Carvalho M. and Costa A. (2014). Graph Cut and Image Segmentation using Mean Cut by Means of an Agglomerative Algorithm . 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 708-712. DOI: 10.5220/0004858207080712

in Bibtex Style

@conference{visapp14,
author={Elaine Ayumi Chiba and Marco Antonio Garcia Carvalho and André Luís Costa},
title={Graph Cut and Image Segmentation using Mean Cut by Means of an Agglomerative Algorithm},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={708-712},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004858207080712},
isbn={978-989-758-003-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Graph Cut and Image Segmentation using Mean Cut by Means of an Agglomerative Algorithm
SN - 978-989-758-003-1
AU - Chiba E.
AU - Carvalho M.
AU - Costa A.
PY - 2014
SP - 708
EP - 712
DO - 10.5220/0004858207080712