Ant Weight Lifting algorithm for image segmentation

Image segmentation forms a quintessential concept and is one of the most in-demand arenas of research in the field of image processing. Throughout the years several techniques like k-means clustering, watershed segmentation and quad tree segmentation have been devised to properly segment an image into well-defined classes. Segmentation techniques can be broadly classified as thresholding techniques, edge detection techniques, clustering, region based and matching. Image segmentation may be the ultimate output desired and may also be a penultimate step in the algorithm. In either case it becomes essential to get an accurately segmented image which is often not the case with the existing algorithms since each algorithm has its own drawback. In our paper we have proposed a novel segmentation technique that is bio-inspired from the behavioral nature of ants and is hence called the Ant Weight Lifting (AWL) segmentation algorithm. Our segmentation algorithm has generated optimum results on a wide range of test cases imposed by us with a high correlation factor between the original and segmented image and also an added perk in the form of a low time complexity.