Improving Image Segmentation Quality Via Graph Theory

DOI

10.2991/isci-15.2015.90How to use a DOI?

Keywords

Semi-Supervised; Graph Theory; Over-segmented

Abstract

Image segmentation is a fundamental process in many image, video, and computer vision applications. It is very essential and critical to image processing and pattern recognition, and determines the quality of final result of analysis and recognition. This paper presents a semi-supervised strategy to deal with the issue of image segmentation. Each image is first segmented coarsely, and represented as a graph model. Then, a semi-supervised algorithm is utilized to estimate the relevance between labeled nodes and unlabeled nodes to construct a relevance matrix. Finally, a normalized cut criterion is utilized to segment images into meaningful units. The experimental results conducted on Berkeley image databases and MSRC image databases demonstrate the effectiveness of the proposed strategy.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiangxiang Li
AU  - Songhao Zhu
PY  - 2015/01
DA  - 2015/01
TI  - Improving Image Segmentation Quality Via Graph Theory
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 676
EP  - 681
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.90
DO  - 10.2991/isci-15.2015.90
ID  - Li2015/01
ER  -