Automatic image segmentation using grid-based Ncut and RAG
by D.N.D. Harini; D. Lalitha Bhaskari
International Journal of Image Mining (IJIM), Vol. 1, No. 4, 2015

Abstract: Irrefutably normalised cuts (Ncut) is one of the most popular segmentation algorithms in computer vision. Inspite of its computational complexity, it has been applied to a wide range of segmentation tasks in computer vision for achieving good segmentation results. The difficulty in Ncut algorithm lies in handling images with high resolution and a predefined number of segments which results in increasing the complexity of the algorithm. In this paper, the drawbacks of the Ncut algorithm are studied and an efficient methodology for automatic image segmentation is proposed using grid based Ncut and merging of regions using region adjacency graph (RAG). The proposed algorithm is successful in two aspects, one in reducing the time complexity and second in automatically determining the number of image segments. The proposed methodology is implemented in three phases which includes preprocessing, segmentation and merging phases. The datasets used for experimentation are NSU, Berkeley, Corel 5K datasets and a few images from our own collection. Various experimental results performed on several images proved to be efficient in reducing the computational complexity and thus increasing the performance of segmentation when compared to standard Ncut algorithms.

Online publication date: Tue, 29-Dec-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Image Mining (IJIM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email