Color Image Segmentation Using Improved Method of Normal Cut
Adnan Ahmad, Guo Ling, Hassan Hayat
Available Online June 2017.
- https://doi.org/10.2991/caai-17.2017.130How to use a DOI?
- grouping; image segmentation; graph partitioning; normalized cuts (ncut); graph theory
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 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 - https://doi.org/10.2991/caai-17.2017.130 ID - Ahmad2017/06 ER -