Color Image Segmentation Using Improved Method of Normal Cut

DOI

10.2991/caai-17.2017.130How to use a DOI?

Keywords

grouping; image segmentation; graph partitioning; normalized cuts (ncut); graph theory

Abstract

Image segmentation is a key problem in several fields of science and technology, for E.g. medicine, robotics, and industrial development. The importance of perceptual grouping and organization in vision possess numerous significant factors, such as similarity, proximity, and good continuation, which lead towards the visual grouping in present. However, even to this day, many of the computational issues of perceptual grouping have remained unresolved. This article presents image segmentation as a graph partitioning problem and proposed modified method of normalized cuts, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. The proposed algorithm deals with the color images which give more clear understanding of an image as compared to the black and white images. The experimental results show that the proposed algorithm gives better, clear and more visible understanding of UAV images.

Copyright

© 2017, 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  - Adnan Ahmad
AU  - Guo Ling
AU  - Hassan Hayat
PY  - 2017/06
DA  - 2017/06
TI  - Color Image Segmentation Using Improved Method of Normal Cut
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 585
EP  - 588
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.130
DO  - 10.2991/caai-17.2017.130
ID  - Ahmad2017/06
ER  -