Authors: D.N.D. Harini; D. Lalitha Bhaskari
Addresses: Department of CS&SE, Andhra University, Visakhapatnam, AP, India ' Department of CS&SE, Andhra University, Visakhapatnam, AP, India
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.
Keywords: image analysis; automatic image segmentation; normalised cuts; Ncut; image grids; colour identification; region adjacency graph; RAG; Gaussian distribution; computer vision.
International Journal of Image Mining, 2015 Vol.1 No.4, pp.279 - 296
Available online: 28 Dec 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article