(untitled) (International Journal of Engineering and Computer Science, 2018)
: Image segmentation is the process that partitions an image into region. Although many literatures studied automated image segmentation, it is still difficult to segment region-of-interest in any kind of images. Thus, manual delineation is important yet. [2] In order to shorten the processing time and to decrease the effort of users, this paper introduces the approaches of interactive image segmentation method based on MRF and Ant colony optimization. In this paper we proposed a segmentation algorithm combined MRF with ACS, which not only applied ACS as optimization algorithm but also introduced the neighborhood pheromone interaction rules into ACS under MRF model. Interactive segmentation aims to separate an object of interest from the rest of an image. This problem in computer vision is known to be hard, and very few fully automatic vision systems exist which have been shown to be accurate and robust under all sorts of challenging inputs. Most of the previous works require users to trace the whole boundary of the object. When the object has a complicated boundary, or the object is in a highly textured region, users have to put great effort into iteratively correcting the selection. [1] Dirichlet Process Multiple-View Learning (DPMVL) for image segmentation technique produces very effective segmentation results as compare to previously existing techniques. DPMVL use MRF model for smoothing the segmentation. This can be further improved by using MRF-based image segmentation using Ant Colony System which works effectively and provide an alternative computational algorithm for building interactive image editing tools. In this paper, we present an interactive segmentation framework that integrates image appearance and boundary constraints in a principled way using combined MRF and ant colony optimization. We have improved proposed technique by using modified technology which have more interactivity, user control of segmentation process, and reach a satisfied result among the noise restraint, edge preservation and computation complexity. Experimental results are provided to demonstrate the superior performance of the proposed approach. A comparison with other standard operators is also discussed and the proposed method produced acceptable results within reasonable amounts of time. It is shown that the proposed algorithm based on ant colony optimization and MRF achieves better performance compared to the typical interactive image segmentation methods without using ant colony optimization concept.